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Glossary of VSA attributes
This Glossary alphabetically lists all attributes used in the VSAv20240809 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20240809 Schema Browser (other Browser versions).

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

C
Name	Schema Table	Database	Description	Type	Length	Unit	Default Value	Unified Content Descriptor
C1	mgcGalaxyStruct	MGC	Concentration index alpha=1	real	4		99.99
c1	ravedr5Source	RAVE	1.st minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c10	ravedr5Source	RAVE	10.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c11	ravedr5Source	RAVE	11.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c12	ravedr5Source	RAVE	12.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c13	ravedr5Source	RAVE	13.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c14	ravedr5Source	RAVE	14.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c15	ravedr5Source	RAVE	15.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c16	ravedr5Source	RAVE	16.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c17	ravedr5Source	RAVE	17.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c18	ravedr5Source	RAVE	18.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w] )	varchar	6			meta.code
c19	ravedr5Source	RAVE	19.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
C2	mgcGalaxyStruct	MGC	Concentration index alpha=2	real	4		99.99
c2	ravedr5Source	RAVE	2.nd minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c20	ravedr5Source	RAVE	20.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
C3	mgcGalaxyStruct	MGC	Concentration index alpha=3	real	4		99.99
c3	ravedr5Source	RAVE	3.rd minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
C4	mgcGalaxyStruct	MGC	Concentration index alpha=4	real	4		99.99
c4	ravedr5Source	RAVE	4.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c5	ravedr5Source	RAVE	5.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c6	ravedr5Source	RAVE	6.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c7	ravedr5Source	RAVE	7.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c8	ravedr5Source	RAVE	8.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c9	ravedr5Source	RAVE	9.th minimum distance to base spectrum given by one of the types (enumeration [a,b,c,d,e,g,h,n,o,p,t,u,w])	varchar	6			meta.code
c_strat	twomass_scn	TWOMASS	Flag indicating the calibration strategy for this night's data.	smallint	2			meta.code
c_strat	twomass_sixx2_scn	TWOMASS	calibration strategy, north only (0=old, 1=new)	smallint	2
camNum	MultiframeDetector	SHARKSv20210222	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	SHARKSv20210421	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	ULTRAVISTADR4	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSDR1	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSDR2	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSDR3	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSDR4	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSDR5	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSDR6	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20120926	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20130417	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20140409	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20150108	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20160114	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20160507	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20170630	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20180419	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20201209	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20231101	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VHSv20240731	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIDEODR2	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIDEODR3	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIDEODR4	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIDEODR5	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIDEOv20100513	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIDEOv20111208	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGDR2	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGDR3	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGDR4	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20110714	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20111019	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20130417	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20140402	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20150421	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20151230	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20160406	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20161202	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VIKINGv20170715	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCDEEPv20230713	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCDEEPv20240506	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCDR1	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCDR2	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCDR3	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCDR4	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCDR5	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20110816	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20110909	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20120126	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20121128	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20130304	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20130805	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20140428	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20140903	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20150309	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20151218	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20160311	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20160822	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20170109	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20170411	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20171101	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20180702	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20181120	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20191212	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20210708	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20230816	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VMCv20240226	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VVVDR1	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VVVDR2	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VVVDR5	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VVVXDR1	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VVVv20100531	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	MultiframeDetector	VVVv20110718	Number of VISTA camera (1 to 16) {image extension keyword: HIERARCH ESO DET CHIP NO}	smallint	2		-9999	obs.field
camNum	SectionDetectors	SHARKSv20210222	UID of camera detector	smallint	2
camNum	SectionDetectors	SHARKSv20210421	UID of camera detector	smallint	2
camNum	SectionDetectors	ULTRAVISTADR4	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSDR3	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSDR4	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSDR5	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSDR6	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSv20150108	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSv20160114	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSv20160507	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSv20170630	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSv20180419	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSv20201209	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSv20231101	UID of camera detector	smallint	2
camNum	SectionDetectors	VHSv20240731	UID of camera detector	smallint	2
camNum	SectionDetectors	VIDEODR4	UID of camera detector	smallint	2
camNum	SectionDetectors	VIDEODR5	UID of camera detector	smallint	2
camNum	SectionDetectors	VIKINGDR4	UID of camera detector	smallint	2
camNum	SectionDetectors	VIKINGv20150421	UID of camera detector	smallint	2
camNum	SectionDetectors	VIKINGv20151230	UID of camera detector	smallint	2
camNum	SectionDetectors	VIKINGv20160406	UID of camera detector	smallint	2
camNum	SectionDetectors	VIKINGv20161202	UID of camera detector	smallint	2
camNum	SectionDetectors	VIKINGv20170715	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCDEEPv20230713	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCDEEPv20240506	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCDR3	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCDR4	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCDR5	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20140428	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20140903	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20150309	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20151218	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20160311	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20160822	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20170109	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20170411	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20171101	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20180702	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20181120	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20191212	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20210708	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20230816	UID of camera detector	smallint	2
camNum	SectionDetectors	VMCv20240226	UID of camera detector	smallint	2
camNum	SectionDetectors	VVVDR5	UID of camera detector	smallint	2
camNum	SectionDetectors	VVVXDR1	UID of camera detector	smallint	2
camNum	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	Number of VISTA camera (1 to 16)	smallint	2		-9999	obs.field
casuVers	Multiframe	SHARKSv20210222	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	SHARKSv20210421	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	ULTRAVISTADR4	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSDR1	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSDR2	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSDR3	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSDR4	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSDR5	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSDR6	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20120926	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20130417	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20140409	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20150108	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20160114	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20160507	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20170630	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20180419	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20201209	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20231101	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VHSv20240731	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIDEODR2	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIDEODR3	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIDEODR4	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIDEODR5	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIDEOv20100513	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIDEOv20111208	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGDR2	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGDR3	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGDR4	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20110714	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20111019	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20130417	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20140402	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20150421	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20151230	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20160406	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20161202	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VIKINGv20170715	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCDEEPv20230713	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCDEEPv20240506	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCDR1	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCDR2	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCDR3	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCDR4	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCDR5	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20110816	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20110909	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20120126	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20121128	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20130304	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20130805	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20140428	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20140903	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20150309	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20151218	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20160311	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20160822	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20170109	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20170411	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20171101	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20180702	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20181120	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20191212	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20210708	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20230816	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VMCv20240226	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VVVDR1	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VVVDR2	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VVVDR5	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VVVXDR1	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VVVv20100531	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	Multiframe	VVVv20110718	CASU Release Version Number {image primary HDU keyword: CASUVERS}	varchar	64		NONE	meta.id
casuVers	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	CASU Release Version Number	varchar	64		NONE	meta.id
cat	twomass_sixx2_psc, twomass_sixx2_scn, twomass_sixx2_xsc	TWOMASS	Catalog indicator 1=catalog, 0=not catalog	smallint	2
catalogue	vmcCepheidVariables	VMCDR4	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20160311	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20160822	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20170109	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20170411	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20171101	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20180702	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20181120	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20191212	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20210708	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20230816	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcCepheidVariables	VMCv20240226	Name of the catalogue containing the counterparts of the VMC Cephs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCDR4	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20140903	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20150309	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20151218	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20160311	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20160822	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20170109	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20170411	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20171101	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20180702	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20181120	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20191212	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20210708	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20230816	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcEclipsingBinaryVariables	VMCv20240226	Name of the catalogue containing the counterparts of the VMC EBs: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCDR4	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20160822	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20170109	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20170411	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20171101	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20180702	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20181120	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20191212	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20210708	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogue	vmcRRlyraeVariables	VMCv20230816	Name of the catalogue containing the counterparts of the VMC RRLyr: OGLE III, EROS-2 or both OGLE III /EROS-2 {catalogue TType keyword: CATALOGUE}	varchar	16		NONE	meta.id
catalogueID	MapCatalogueDetector	SHARKSv20210222	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	SHARKSv20210421	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	ULTRAVISTADR4	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VHSv20201209	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VHSv20231101	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VHSv20240731	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VMCDEEPv20230713	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VMCDEEPv20240506	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VMCDR5	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VMCv20191212	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VMCv20210708	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VMCv20230816	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VMCv20240226	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VVVDR5	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapCatalogueDetector	VVVXDR1	Unique identifier for catalogue	bigint	8		-99999999	meta_id
catalogueID	MapFrameStatus	SHARKSv20210222	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	SHARKSv20210421	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VHSv20201209	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VHSv20231101	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VHSv20240731	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VMCDEEPv20230713	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VMCDEEPv20240506	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VMCDR5	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VMCv20191212	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VMCv20210708	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VMCv20230816	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VMCv20240226	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VVVDR5	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus	VVVXDR1	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapFrameStatus, ultravistaMapRemeasAver	ULTRAVISTADR4	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	MapProvenance	ULTRAVISTADR4	the UID of the component frame	bigint	8			obs.field
catalogueID	SExtractorInputParams	SHARKSv20210222	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	SHARKSv20210421	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	ULTRAVISTADR4	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VHSv20201209	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VHSv20231101	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VHSv20240731	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VMCDEEPv20230713	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VMCDEEPv20240506	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VMCDR5	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VMCv20191212	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VMCv20210708	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VMCv20230816	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VMCv20240226	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VVVDR5	UID of the catalogue	bigint	8
catalogueID	SExtractorInputParams	VVVXDR1	UID of the catalogue	bigint	8
catalogueID	ultravistaMapAverageWeights	ULTRAVISTADR4	Unique identifier for input catalogue	bigint	8		-99999999	meta_id
catalogueID	ultravistaMapRemeasurement	ULTRAVISTADR4	Unique identifier for catalogue {catalogue extension keyword:  CATLGID}	bigint	8		-99999999	meta_id;meta_main
catalogueID	vikingMapRemeasAver	VIKINGZYSELJv20160909	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	vikingMapRemeasAver	VIKINGZYSELJv20170124	Unique identifier for catalogue	bigint	8		-99999999	meta_id;meta_main
catalogueID	vikingMapRemeasurement	VIKINGZYSELJv20160909	Unique identifier for catalogue {catalogue extension keyword:  CATLGID}	bigint	8		-99999999	meta_id;meta_main
catalogueID	vikingMapRemeasurement	VIKINGZYSELJv20170124	Unique identifier for catalogue {catalogue extension keyword:  CATLGID}	bigint	8		-99999999	meta_id;meta_main
catalogueSchema	Programme	SHARKSv20210222	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	SHARKSv20210421	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	ULTRAVISTADR4	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSDR1	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSDR2	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSDR3	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSDR4	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSDR5	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSDR6	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20120926	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20130417	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20150108	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20160114	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20160507	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20170630	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20180419	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20201209	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20231101	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VHSv20240731	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIDEODR2	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIDEODR3	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIDEODR4	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIDEODR5	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIDEOv20100513	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIDEOv20111208	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGDR2	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGDR3	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGDR4	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGv20110714	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGv20111019	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGv20130417	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGv20150421	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGv20151230	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGv20160406	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGv20161202	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VIKINGv20170715	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCDEEPv20230713	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCDEEPv20240506	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCDR1	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCDR3	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCDR4	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCDR5	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20110816	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20110909	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20120126	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20121128	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20130304	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20130805	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20140428	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20140903	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20150309	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20151218	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20160311	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20160822	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20170109	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20170411	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20171101	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20180702	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20181120	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20191212	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20210708	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20230816	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VMCv20240226	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VSAQC	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VVVDR1	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VVVDR2	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VVVDR5	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VVVXDR1	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VVVv20100531	Script file that describes the catalogue schema	varchar	64			??
catalogueSchema	Programme	VVVv20110718	Script file that describes the catalogue schema	varchar	64			??
catCreationDate	MultiframeDetector	SHARKSv20210222	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	SHARKSv20210421	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	ULTRAVISTADR4	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSDR1	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSDR2	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSDR3	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSDR4	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSDR5	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSDR6	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20120926	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20130417	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20140409	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20150108	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20160114	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20160507	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20170630	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20180419	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20201209	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20231101	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VHSv20240731	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIDEODR2	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIDEODR3	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIDEODR4	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIDEODR5	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIDEOv20100513	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIDEOv20111208	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGDR2	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGDR3	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGDR4	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20110714	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20111019	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20130417	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20140402	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20150421	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20151230	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20160406	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20161202	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VIKINGv20170715	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCDEEPv20230713	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCDEEPv20240506	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCDR1	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCDR2	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCDR3	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCDR4	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCDR5	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20110816	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20110909	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20120126	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20121128	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20130304	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20130805	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20140428	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20140903	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20150309	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20151218	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20160311	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20160822	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20170109	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20170411	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20171101	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20180702	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20181120	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20191212	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20210708	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20230816	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VMCv20240226	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VVVDR1	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VVVDR2	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VVVDR5	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VVVXDR1	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VVVv20100531	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	MultiframeDetector	VVVv20110718	Creation date/time of catalogue file {catalogue extension keyword:  DATE}	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catCreationDate	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	Creation date/time of catalogue file	datetime	8	MM-DD-YYYY:hh:mm:ss.sss	12-31-9999	??
catFlag	vmcMLClassificationCatalogue	VMCv20240226	Flag indicating which catalogue the source belongs to. 'H' = High-confidence (>80%), 'M' = Mid-confidence (>60%, <80%) and 'L' = Low-confidence (<60%). {catalogue TType keyword: Cat_flag}	varchar	1
CATNAME	agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss, supercos, twomass	SIXDF	supplied catalogue name	varchar	15
catName	MapFrameStatus	SHARKSv20210222	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	SHARKSv20210421	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	ULTRAVISTADR4	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VHSv20201209	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VHSv20231101	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VHSv20240731	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VMCDEEPv20230713	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VMCDEEPv20240506	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VMCDR5	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VMCv20191212	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VMCv20210708	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VMCv20230816	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VMCv20240226	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VVVDR5	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	MapFrameStatus	VVVXDR1	the filename of the associated catalogue MEF, eg. server:/path/filename_list.fits	varchar	256		NONE	meta.id;meta.dataset
catName	Multiframe	SHARKSv20210222	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	SHARKSv20210421	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	ULTRAVISTADR4	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSDR1	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSDR2	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSDR3	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSDR4	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSDR5	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSDR6	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20120926	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20130417	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20140409	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20150108	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20160114	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20160507	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20170630	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20180419	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20201209	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20231101	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VHSv20240731	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIDEODR2	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIDEODR3	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIDEODR4	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIDEODR5	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIDEOv20100513	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIDEOv20111208	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGDR2	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGDR3	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGDR4	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20110714	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20111019	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20130417	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20140402	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20150421	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20151230	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20160406	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20161202	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VIKINGv20170715	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCDEEPv20230713	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCDEEPv20240506	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCDR1	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCDR2	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCDR3	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCDR4	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCDR5	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20110816	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20110909	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20120126	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20121128	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20130304	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20130805	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20140428	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20140903	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20150309	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20151218	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20160311	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20160822	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20170109	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20170411	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20171101	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20180702	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20181120	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20191212	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20210708	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20230816	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VMCv20240226	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VVVDR1	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VVVDR2	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VVVDR5	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VVVXDR1	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VVVv20100531	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	Multiframe	VVVv20110718	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256		'NONE'	meta.id;meta.dataset
catName	iras_assoc_cats	IRAS	Source Name	varchar	32			meta.id;meta.main
catName	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	the filename of the associated catalogue MEF, eg. server:/path/filename.fits	varchar	256			meta.id;meta.dataset
catNo	iras_asc, iras_assoc_cats	IRAS	Catalog number	tinyint	1			meta.id;meta.dataset
cc_100	iras_psc	IRAS	point source correlation coefficient (100 micron).	varchar	1			meta.note
cc_12	iras_psc	IRAS	point source correlation coefficient (12 micron).	varchar	1			meta.note
cc_25	iras_psc	IRAS	point source correlation coefficient (25 micron).	varchar	1			meta.note
cc_60	iras_psc	IRAS	point source correlation coefficient (60 micron).	varchar	1			meta.note
cc_flags	allwise_sc	WISE	Contamination and confusion flag. Four character string, one character per band [W1/W2/W3/W4], that indicates that the photometry and/or position measurements of a source may be contaminated or biased due to proximity to an image artifact. CAUTION: Non-zero cc_flags values in any band indicate the the measurement in that band may be contaminated and the photometry should be used with caution.	varchar	4
			The type of artifact that may contaminate the measurements is denoted by the following codes. Lower-case letters correspond to instances in which the source detection in a band is believed to be real but the measurement may be contaminated by the artifact. Upper-case letters are instances in which the source detection in a band may be a spurious detection of an artifact. D,d - Diffraction spike. Source may be a spurious detection of (D) or contaminated by (d) a diffraction spike from a nearby bright star on the same image, or P,p - Persistence. Source may be a spurious detection of (P) or contaminated by (p) a short-term latent image left by a bright source, or H,h - Halo. Source may be a spurious detection of (H) or contaminated by (h) the scattered light halo surrounding a nearby bright source, or O,o (letter "o") - Optical ghost. Source may be a spurious detection of (O) or contaminated by (o) an optical ghost image caused by a nearby bright source, or 0 (number zero) - Source is unaffected by known artifacts. A source extraction may be affected by more than one type of artifact or condition. In this event, the cc_flags value in each band is set in the following priority order: D,P,H,O,d,p,h,o,0. The full tally of artifacts affecting a source in each band is given in the w?cc_map and w?cc_map_str columns. A source can appear in the AllWISE Source Catalog even if it is flagged as a spurious artifact detection in a band if there is a reliable detection in another band that is not flagged as a spurious artifact detection. Entries in the AllWISE Reject Table may be flagged as spurious detections of artifacts in all bands.
cc_flags	catwise_2020, catwise_prelim	WISE	worst case 4 character cc_flag from AllWISE	varchar	16
cc_flags	wise_allskysc	WISE	Contamination and confusion flag. Four character strong, one character per band [W1/W2/W3/W4], that indicates that the photometry and/or position measurements of a source may be contaminated or biased due to proximity to an image artifact. CAUTION: Non-zero cc_flags values in any band indicate the the measurement in that band may be contaminated and the photometry should be used with caution.	char	4
cc_flags	wise_prelimsc	WISE	Contamination and confusion flag Four character strong, one character per band [W1/W2/W3/W4], that indicates that the photometry and/or position measurements of a source may be contaminated or biased due to proximity to an image artifact	char	4
cc_flags_ALLWISE	ravedr5Source	RAVE	prioritized artifacts affecting the source in each band	varchar	5			meta.code
cc_flg	twomass_psc	TWOMASS	Contamination and confusion flag.	varchar	3			meta.code
cc_flg	twomass_sixx2_psc	TWOMASS	indicates JHK artifact contamination and/or confusion	varchar	3
cc_flg	twomass_sixx2_xsc	TWOMASS	indicates artifact contamination and/or confusion	varchar	1
cc_flg	twomass_xsc	TWOMASS	indicates artifact contamination and/or confusion.	varchar	1			meta.code
CCD	mgcDetection	MGC	CCD number	tinyint	1
CCD_R	spectra	SIXDF	R CCD name	varchar	12
CCD_V	spectra	SIXDF	V CCD name	varchar	12
ccdm	tycho2	GAIADR1	CCDM component identifier for Hipparcos stars	varchar	4			meta.code.multip
cd11	CurrentAstrometry	SHARKSv20210222	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	SHARKSv20210421	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	ULTRAVISTADR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSDR6	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20120926	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20130417	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20140409	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20150108	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20160114	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20160507	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20170630	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20180419	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20201209	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20231101	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VHSv20240731	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIDEODR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIDEODR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIDEODR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIDEODR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIDEOv20100513	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIDEOv20111208	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGv20110714	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGv20111019	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGv20130417	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGv20140402	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGv20150421	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9
cd11	CurrentAstrometry	VIKINGv20151230	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGv20160406	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGv20161202	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VIKINGv20170715	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCDEEPv20230713	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCDEEPv20240506	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20110816	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20110909	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20120126	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20121128	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20130304	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20130805	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20140428	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20140903	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20150309	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20151218	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20160311	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20160822	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20170109	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20170411	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20171101	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20180702	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20181120	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20191212	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20210708	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20230816	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VMCv20240226	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VVVDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VVVDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VVVDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VVVXDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VVVv20100531	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	CurrentAstrometry	VVVv20110718	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd11	sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry	VSAQC	Element of the linear transformation matrix (with scale)	float	8			pos.wcs.cdmatrix
cd12	CurrentAstrometry	SHARKSv20210222	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	SHARKSv20210421	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	ULTRAVISTADR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSDR6	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20120926	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20130417	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20140409	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20150108	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20160114	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20160507	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20170630	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20180419	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20201209	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20231101	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VHSv20240731	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIDEODR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIDEODR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIDEODR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIDEODR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIDEOv20100513	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIDEOv20111208	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGv20110714	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGv20111019	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGv20130417	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGv20140402	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGv20150421	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9
cd12	CurrentAstrometry	VIKINGv20151230	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGv20160406	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGv20161202	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VIKINGv20170715	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCDEEPv20230713	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCDEEPv20240506	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20110816	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20110909	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20120126	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20121128	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20130304	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20130805	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20140428	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20140903	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20150309	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20151218	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20160311	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20160822	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20170109	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20170411	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20171101	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20180702	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20181120	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20191212	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20210708	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20230816	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VMCv20240226	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VVVDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VVVDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VVVDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VVVXDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VVVv20100531	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	CurrentAstrometry	VVVv20110718	Element of the linear transformation matrix (with scale) {image extension keyword: CD1_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd12	sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry	VSAQC	Element of the linear transformation matrix (with scale)	float	8			pos.wcs.cdmatrix
cd21	CurrentAstrometry	SHARKSv20210222	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	SHARKSv20210421	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	ULTRAVISTADR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSDR6	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20120926	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20130417	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20140409	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20150108	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20160114	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20160507	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20170630	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20180419	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20201209	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20231101	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VHSv20240731	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIDEODR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIDEODR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIDEODR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIDEODR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIDEOv20100513	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIDEOv20111208	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGv20110714	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGv20111019	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGv20130417	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGv20140402	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGv20150421	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9
cd21	CurrentAstrometry	VIKINGv20151230	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGv20160406	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGv20161202	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VIKINGv20170715	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCDEEPv20230713	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCDEEPv20240506	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20110816	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20110909	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20120126	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20121128	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20130304	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20130805	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20140428	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20140903	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20150309	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20151218	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20160311	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20160822	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20170109	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20170411	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20171101	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20180702	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20181120	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20191212	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20210708	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20230816	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VMCv20240226	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VVVDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VVVDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VVVDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VVVXDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VVVv20100531	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	CurrentAstrometry	VVVv20110718	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_1}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd21	sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry	VSAQC	Element of the linear transformation matrix (with scale)	float	8			pos.wcs.cdmatrix
cd22	CurrentAstrometry	SHARKSv20210222	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	SHARKSv20210421	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	ULTRAVISTADR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSDR6	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20120926	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20130417	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20140409	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20150108	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20160114	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20160507	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20170630	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20180419	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20201209	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20231101	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VHSv20240731	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIDEODR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIDEODR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIDEODR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIDEODR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIDEOv20100513	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIDEOv20111208	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGv20110714	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGv20111019	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGv20130417	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGv20140402	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGv20150421	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9
cd22	CurrentAstrometry	VIKINGv20151230	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGv20160406	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGv20161202	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VIKINGv20170715	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCDEEPv20230713	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCDEEPv20240506	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCDR3	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCDR4	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20110816	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20110909	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20120126	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20121128	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20130304	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20130805	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20140428	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20140903	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20150309	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20151218	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20160311	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20160822	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20170109	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20170411	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20171101	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20180702	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20181120	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20191212	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20210708	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20230816	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VMCv20240226	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VVVDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VVVDR2	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VVVDR5	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VVVXDR1	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VVVv20100531	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	CurrentAstrometry	VVVv20110718	Element of the linear transformation matrix (with scale) {image extension keyword: CD2_2}	float	8		-0.9999995e9	pos.wcs.cdmatrix
cd22	sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry	VSAQC	Element of the linear transformation matrix (with scale)	float	8			pos.wcs.cdmatrix
CENDEC_R	spectra	SIXDF	DEC of field centre	float	8	radians
CENDEC_V	spectra	SIXDF	DEC of field centre	float	8	radians
CENRA_R	spectra	SIXDF	RA of field centre	float	8	radians
CENRA_V	spectra	SIXDF	RA of field centre	float	8	radians
centralDec	CurrentAstrometry	SHARKSv20210222	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	SHARKSv20210421	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	ULTRAVISTADR4	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSDR1	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSDR2	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSDR3	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSDR4	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSDR5	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSDR6	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20120926	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20130417	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20140409	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20150108	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20160114	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20160507	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20170630	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20180419	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20201209	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20231101	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VHSv20240731	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIDEODR2	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIDEODR3	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIDEODR4	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIDEODR5	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIDEOv20100513	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIDEOv20111208	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGDR2	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGDR3	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGDR4	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20110714	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20111019	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20130417	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20140402	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20150421	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20151230	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20160406	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20161202	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VIKINGv20170715	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCDEEPv20230713	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCDEEPv20240506	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCDR1	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCDR2	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCDR3	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCDR4	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCDR5	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20110816	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20110909	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20120126	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20121128	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20130304	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20130805	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20140428	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20140903	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20150309	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20151218	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20160311	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20160822	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20170109	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20170411	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20171101	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20180702	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20181120	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20191212	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20210708	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20230816	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VMCv20240226	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VVVDR1	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VVVDR2	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VVVDR5	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VVVXDR1	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VVVv20100531	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	CurrentAstrometry	VVVv20110718	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralDec	ExternalProductCatalogue	SHARKSv20210222	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	SHARKSv20210421	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	ULTRAVISTADR4	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSDR3		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSDR4		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSDR5	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSDR6	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSv20150108		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSv20160114	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSv20160507	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSv20170630	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSv20180419	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSv20201209	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSv20231101	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VHSv20240731	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VIDEODR4		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VIDEODR5		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VIKINGDR4		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VIKINGv20150421		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VIKINGv20151230	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VIKINGv20160406	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VIKINGv20161202	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VIKINGv20170715	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCDEEPv20230713	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCDEEPv20240506	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCDR3		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCDR4	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCDR5	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20140428		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20140903		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20150309		float	8	Central declination of field -/U deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20151218	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20160311	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20160822	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20170109	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20170411	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20171101	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20180702	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20181120	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20191212	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20210708	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20230816	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VMCv20240226	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VVVDR5	Central declination of field	float	8	deg	-0.9999995e9
centralDec	ExternalProductCatalogue	VVVXDR1	Central declination of field	float	8	deg	-0.9999995e9
centralDec	sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry	VSAQC	Dec (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
centralRa	CurrentAstrometry	SHARKSv20210222	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	SHARKSv20210421	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	ULTRAVISTADR4	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSDR1	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSDR2	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSDR3	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSDR4	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSDR5	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSDR6	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20120926	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20130417	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20140409	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20150108	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20160114	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20160507	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20170630	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20180419	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20201209	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20231101	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VHSv20240731	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIDEODR2	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIDEODR3	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIDEODR4	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIDEODR5	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIDEOv20100513	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIDEOv20111208	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGDR2	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGDR3	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGDR4	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20110714	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20111019	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20130417	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20140402	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20150421	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20151230	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20160406	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20161202	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VIKINGv20170715	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCDEEPv20230713	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCDEEPv20240506	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCDR1	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCDR2	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCDR3	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCDR4	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCDR5	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20110816	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20110909	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20120126	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20121128	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20130304	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20130805	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20140428	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20140903	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20150309	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20151218	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20160311	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20160822	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20170109	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20170411	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20171101	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20180702	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20181120	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20191212	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20210708	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20230816	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VMCv20240226	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VVVDR1	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VVVDR2	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VVVDR5	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VVVXDR1	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VVVv20100531	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	CurrentAstrometry	VVVv20110718	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
centralRa	ExternalProductCatalogue	SHARKSv20210222	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	SHARKSv20210421	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	ULTRAVISTADR4	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSDR3		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSDR4		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSDR5	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSDR6	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSv20150108		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSv20160114	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSv20160507	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSv20170630	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSv20180419	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSv20201209	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSv20231101	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VHSv20240731	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VIDEODR4		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VIDEODR5		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VIKINGDR4		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VIKINGv20150421		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VIKINGv20151230	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VIKINGv20160406	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VIKINGv20161202	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VIKINGv20170715	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCDEEPv20230713	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCDEEPv20240506	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCDR3		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCDR4	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCDR5	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20140428		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20140903		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20150309		float	8	Central right-ascension of field -/U deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20151218	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20160311	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20160822	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20170109	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20170411	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20171101	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20180702	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20181120	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20191212	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20210708	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20230816	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VMCv20240226	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VVVDR5	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	ExternalProductCatalogue	VVVXDR1	Central right-ascension of field	float	8	deg	-0.9999995e9
centralRa	sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry	VSAQC	RA (J2000) at device centre	float	8	Degrees	-0.9999995e9	pos.eq.ra;meta.main
cephMode	vmcCepheidVariables	VMCDR3	Type of Cepheid {catalogue TType keyword: MODE}	varchar	5		NONE	meta.code.class
cephMode	vmcCepheidVariables	VMCDR4	Type of Cepheid {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20121128	Type of Cepheid {catalogue TType keyword: MODE}	varchar	5		NONE	meta.code.class
cephMode	vmcCepheidVariables	VMCv20140428	Type of Cepheid {catalogue TType keyword: MODE}	varchar	5		NONE	meta.code.class
cephMode	vmcCepheidVariables	VMCv20140903	Type of Cepheid {catalogue TType keyword: MODE}	varchar	5		NONE	meta.code.class
cephMode	vmcCepheidVariables	VMCv20150309	Type of Cepheid {catalogue TType keyword: MODE}	varchar	5		NONE	meta.code.class
cephMode	vmcCepheidVariables	VMCv20151218	Type of Cepheid {catalogue TType keyword: MODE}	varchar	5		NONE	meta.code.class
cephMode	vmcCepheidVariables	VMCv20160311	Type of Cepheid {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20160822	Type of Cepheid {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20170109	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20170411	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20171101	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20180702	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20181120	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20191212	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20210708	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20230816	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephMode	vmcCepheidVariables	VMCv20240226	Mode of Cepheid e.g. F0 {catalogue TType keyword: MODE}	varchar	16		NONE	meta.code.class
			F = Fundamental; FO = First Overtone; SO = Second overtone; TO = Third Overtone.
cephSubType	vmcCepheidVariables	VMCv20170109	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephSubType	vmcCepheidVariables	VMCv20170411	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephSubType	vmcCepheidVariables	VMCv20171101	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephSubType	vmcCepheidVariables	VMCv20180702	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephSubType	vmcCepheidVariables	VMCv20181120	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephSubType	vmcCepheidVariables	VMCv20191212	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephSubType	vmcCepheidVariables	VMCv20210708	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephSubType	vmcCepheidVariables	VMCv20230816	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephSubType	vmcCepheidVariables	VMCv20240226	Sub-type of Cepheid, {catalogue TType keyword: SUBTYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20170109	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20170411	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20171101	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20180702	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20181120	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20191212	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20210708	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20230816	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cephType	vmcCepheidVariables	VMCv20240226	Type of Cepheid, e.g. DCEP {catalogue TType keyword: TYPE}	varchar	16		NONE	meta.code.class
cepID	ogle4CepLmcSource	OGLE	Cepheid ID in the form OGLE-LMC-CEP-NNNN	varchar	18			meta.id
cepID	ogle4CepSmcSource	OGLE	Cepheid ID in the form OGLE-SMC-CEP-NNNN	varchar	18			meta.id
cerr	smashdr2_source	SMASH	Uncertainty of CMAG including errors in calibration	real	4
CHI	mgcGalaxyStruct	MGC	Chi^2 of GIM2D Fit	real	4		99.99
chi	smashdr2_deep, smashdr2_object	SMASH	Average DAOPHOT chi value, i.e. how well the PSF fit this source	real	4
chi	smashdr2_source	SMASH	DAOPHOT chi value, i.e. how well the PSF fit this source	real	4
chi	vvvPsfDaophotJKsSource	VVVDR5	Reduced chi squared of PSF fit. {catalogue TType keyword: chi}	real	4			stat.fit.chi2
chi2	sharksVariability	SHARKSv20210222	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	sharksVariability	SHARKSv20210421	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	ultravistaVariability	ULTRAVISTADR4	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	videoVariability	VIDEODR2	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	videoVariability	VIDEODR3	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	videoVariability	VIDEODR4	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	videoVariability	VIDEODR5	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	videoVariability	VIDEOv20100513	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	videoVariability	VIDEOv20111208	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGDR2	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGDR3	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGDR4	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20110714	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20111019	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20130417	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20140402	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20150421	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20151230	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20160406	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20161202	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vikingVariability	VIKINGv20170715	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCDR1	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCDR2	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCDR3	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCDR4	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCDR5	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20110816	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20110909	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20120126	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20121128	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20130304	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20130805	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20140428	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20140903	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20150309	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20151218	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20160311	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20160822	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20170109	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20170411	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20171101	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20180702	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20181120	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20191212	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20210708	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20230816	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcVariability	VMCv20240226	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcdeepVariability	VMCDEEPv20230713	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vmcdeepVariability	VMCDEEPv20240506	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vvvVariability	VVVDR1	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vvvVariability	VVVDR2	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vvvVariability	VVVDR5	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vvvVariability	VVVv20100531	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2	vvvVariability	VVVv20110718	Chi-squared value of astrometric solution	real	4		-0.9999995e9	stat.fit.chi2
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
chi2pmdec	catwise_2020, catwise_prelim	WISE	chi-square for PMRA difference (1 DF)	real	4
chi2pmra	catwise_2020, catwise_prelim	WISE	chi-square for PMRA difference (1 DF)	real	4
chi2red	combo17CDFSSource	COMBO17	reduced Chi^2 of best-fitting template	real	4
chi2s	sharksAstrometricInfo	SHARKSv20210222	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	sharksAstrometricInfo	SHARKSv20210421	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	ultravistaAstrometricInfo	ULTRAVISTADR4	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vhsAstrometricInfo	VHSDR6	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vhsAstrometricInfo	VHSv20170630	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vhsAstrometricInfo	VHSv20180419	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vhsAstrometricInfo	VHSv20201209	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vhsAstrometricInfo	VHSv20231101	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vhsAstrometricInfo	VHSv20240731	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	videoAstrometricInfo	VIDEODR2	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	videoAstrometricInfo	VIDEODR3	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	videoAstrometricInfo	VIDEODR4	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	videoAstrometricInfo	VIDEODR5	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	videoAstrometricInfo	VIDEOv20111208	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	vikingAstrometricInfo	VIKINGDR2	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	vikingAstrometricInfo	VIKINGDR3	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vikingAstrometricInfo	VIKINGDR4	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vikingAstrometricInfo	VIKINGv20110714	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	vikingAstrometricInfo	VIKINGv20111019	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	vikingAstrometricInfo	VIKINGv20130417	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vikingAstrometricInfo	VIKINGv20140402	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vikingAstrometricInfo	VIKINGv20150421	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vikingAstrometricInfo	VIKINGv20151230	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vikingAstrometricInfo	VIKINGv20160406	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vikingAstrometricInfo	VIKINGv20161202	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vikingAstrometricInfo	VIKINGv20170715	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCDR1	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	vmcAstrometricInfo	VMCDR2	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCDR3	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCDR4	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCDR5	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20110816	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	vmcAstrometricInfo	VMCv20110909	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	vmcAstrometricInfo	VMCv20120126	Reduced chi-squared of fit	float	8		-0.9999995e9	??
chi2s	vmcAstrometricInfo	VMCv20121128	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20130304	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20130805	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20140428	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20140903	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20150309	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20151218	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20160311	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20160822	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20170109	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20170411	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20171101	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20180702	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20181120	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20191212	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20210708	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20230816	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcAstrometricInfo	VMCv20240226	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcdeepAstrometricInfo	VMCDEEPv20230713	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vmcdeepAstrometricInfo	VMCDEEPv20240506	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vvvAstrometricInfo	VVVDR1	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vvvAstrometricInfo	VVVDR2	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vvvAstrometricInfo	VVVDR5	Reduced chi-squared of fit	float	8		-0.9999995e9	stat.fit.chi2
chi2s	vvvAstrometricInfo	VVVv20110718	Reduced chi-squared of fit	float	8		-0.9999995e9	??
CHIL15	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Chi	float	8		99.999
CHIL24	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Chi	float	8		99.999
CHIN3	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Chi	float	8		99.999
chip	smashdr2_source	SMASH	Chip number (1-62)	smallint	2
chipid	smashdr2_source	SMASH	Unique chip image name, e.g. 00123451_10	varchar	16
CHIS11	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Chi	float	8		99.999
CHIS7	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Chi	float	8		99.999
CHISQ_c	ravedr5Source	RAVE	chi square [Chemical pipeline] (Note 2)	real	4			stat.fit.chi2
CHISQ_K	ravedr5Source	RAVE	chi square [Kordopatis Stella Parameter pipeline]	float	8			stat.fit.chi2
CIR_BVAR	MultiframeDetector	SHARKSv20210222	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	SHARKSv20210421	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	ULTRAVISTADR4	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSDR1	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSDR2	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSDR3	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSDR4	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSDR5	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSDR6	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20120926	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20130417	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20140409	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20150108	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20160114	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20160507	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20170630	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20180419	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20201209	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20231101	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VHSv20240731	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIDEODR2	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIDEODR3	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIDEODR4	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIDEODR5	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIDEOv20100513	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIDEOv20111208	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGDR2	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGDR3	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGDR4	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20110714	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20111019	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20130417	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20140402	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20150421	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20151230	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20160406	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20161202	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VIKINGv20170715	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCDEEPv20230713	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCDEEPv20240506	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCDR1	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCDR2	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCDR3	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCDR4	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCDR5	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20110816	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20110909	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20120126	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20121128	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20130304	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20130805	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20140428	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20140903	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20150309	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20151218	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20160311	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20160822	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20170109	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20170411	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20171101	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20180702	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20181120	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20191212	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20210708	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20230816	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VMCv20240226	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VVVDR1	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VVVDR2	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VVVDR5	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VVVXDR1	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VVVv20100531	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	MultiframeDetector	VVVv20110718	Latest estimate of background variance {image extension keyword: CIR_BVAR}	real	4		-0.9999995e9
CIR_BVAR	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	Latest estimate of background variance	real	4		-0.9999995e9
CIRMED	MultiframeDetector	SHARKSv20210222	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	SHARKSv20210421	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	ULTRAVISTADR4	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSDR1	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSDR2	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSDR3	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSDR4	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSDR5	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSDR6	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20120926	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20130417	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20140409	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20150108	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20160114	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20160507	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20170630	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20180419	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20201209	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20231101	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VHSv20240731	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIDEODR2	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIDEODR3	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIDEODR4	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIDEODR5	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIDEOv20100513	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIDEOv20111208	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGDR2	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGDR3	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGDR4	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20110714	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20111019	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20130417	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20140402	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20150421	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20151230	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20160406	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20161202	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VIKINGv20170715	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCDEEPv20230713	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCDEEPv20240506	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCDR1	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCDR2	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCDR3	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCDR4	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCDR5	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20110816	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20110909	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20120126	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20121128	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20130304	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20130805	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20140428	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20140903	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20150309	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20151218	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20160311	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20160822	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20170109	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20170411	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20171101	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20180702	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20181120	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20191212	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20210708	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20230816	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VMCv20240226	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VVVDR1	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VVVDR2	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VVVDR5	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VVVXDR1	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VVVv20100531	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	MultiframeDetector	VVVv20110718	Latest estimate of background {image extension keyword: CIRMED}	real	4		-0.9999995e9
CIRMED	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	Latest estimate of background	real	4		-0.9999995e9
cirr1	iras_psc	IRAS	Number of nearby 100 micron only WSDB sources	tinyint	1			meta.number
cirr2	iras_psc	IRAS	Spatially filtered 100 micron sky brightness ratio to flux density of point source	tinyint	1			phot.flux;arith.ratio
cirr3	iras_psc	IRAS	Total 100 micron sky surface brightness	smallint	2	MJy/sr		phot.flux.density.sb;em.IR.60-100um
cirVers	MultiframeDetector	SHARKSv20210222	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	SHARKSv20210421	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	ULTRAVISTADR4	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSDR1	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSDR2	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSDR3	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSDR4	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSDR5	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSDR6	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20120926	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20130417	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20140409	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20150108	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20160114	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20160507	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20170630	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20180419	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20201209	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20231101	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VHSv20240731	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIDEODR2	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIDEODR3	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIDEODR4	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIDEODR5	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIDEOv20100513	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIDEOv20111208	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGDR2	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGDR3	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGDR4	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20110714	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20111019	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20130417	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20140402	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20150421	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20151230	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20160406	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20161202	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VIKINGv20170715	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCDEEPv20230713	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCDEEPv20240506	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCDR1	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCDR2	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCDR3	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCDR4	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCDR5	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20110816	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20110909	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20120126	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20121128	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20130304	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20130805	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20140428	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20140903	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20150309	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20151218	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20160311	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20160822	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20170109	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20170411	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20171101	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20180702	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20181120	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20191212	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20210708	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20230816	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VMCv20240226	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VVVDR1	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VVVDR2	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VVVDR5	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VVVXDR1	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VVVv20100531	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	MultiframeDetector	VVVv20110718	CIRDR Version {image extension keyword: CIR_VERS}	varchar	32		NONE
cirVers	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	CIRDR Version	varchar	32		NONE
CLASS	mgcDetection	MGC	Classification parameter	smallint	2
class	sharksDetection	SHARKSv20210222	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	sharksDetection	SHARKSv20210421	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	ultravistaDetection	ULTRAVISTADR4	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			Using the SE classification statistic (see the corresponding entry for classStat), a CASU-like discrete classification code is assigned for each detected object as follows: Flag Meaning -9 Saturated -3 Probable galaxy -2 Probable star -1 Star +1 Galaxy where rectangular regions in the magnitude-classStat plane are used to select each class.
class	ultravistaMapRemeasAver	ULTRAVISTADR4	Merged Flag indicating most probable morphological classification -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	ultravistaMapRemeasurement	ULTRAVISTADR4	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			stat
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSDR1	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSDR2	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSDR3	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSDR4	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSDR5	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSDR6	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20120926	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20130417	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20140409	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20150108	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20160114	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20160507	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20170630	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20180419	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20201209	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20231101	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsDetection	VHSv20240731	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vhsListRemeasurement	VHSDR1	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	videoDetection	VIDEODR2	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			Using the SE classification statistic (see the corresponding entry for classStat), a CASU-like discrete classification code is assigned for each detected object as follows: Flag Meaning -9 Saturated -3 Probable galaxy -2 Probable star -1 Star +1 Galaxy where rectangular regions in the magnitude-classStat plane are used to select each class.
class	videoDetection	VIDEODR3	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			Using the SE classification statistic (see the corresponding entry for classStat), a CASU-like discrete classification code is assigned for each detected object as follows: Flag Meaning -9 Saturated -3 Probable galaxy -2 Probable star -1 Star +1 Galaxy where rectangular regions in the magnitude-classStat plane are used to select each class.
class	videoDetection	VIDEODR4	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			Using the SE classification statistic (see the corresponding entry for classStat), a CASU-like discrete classification code is assigned for each detected object as follows: Flag Meaning -9 Saturated -3 Probable galaxy -2 Probable star -1 Star +1 Galaxy where rectangular regions in the magnitude-classStat plane are used to select each class.
class	videoDetection	VIDEODR5	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			Using the SE classification statistic (see the corresponding entry for classStat), a CASU-like discrete classification code is assigned for each detected object as follows: Flag Meaning -9 Saturated -3 Probable galaxy -2 Probable star -1 Star +1 Galaxy where rectangular regions in the magnitude-classStat plane are used to select each class.
class	videoDetection	VIDEOv20100513	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			Using the SE classification statistic (see the corresponding entry for classStat), a CASU-like discrete classification code is assigned for each detected object as follows: Flag Meaning -9 Saturated -3 Probable galaxy -2 Probable star -1 Star +1 Galaxy where rectangular regions in the magnitude-classStat plane are used to select each class.
class	videoDetection	VIDEOv20111208	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			Using the SE classification statistic (see the corresponding entry for classStat), a CASU-like discrete classification code is assigned for each detected object as follows: Flag Meaning -9 Saturated -3 Probable galaxy -2 Probable star -1 Star +1 Galaxy where rectangular regions in the magnitude-classStat plane are used to select each class.
class	videoListRemeasurement	VIDEOv20100513	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGDR2	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGDR3	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGDR4	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20110714	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20111019	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20130417	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20140402	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20150421	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20151230	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20160406	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20161202	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingDetection	VIKINGv20170715	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingListRemeasurement	VIKINGv20110714	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingListRemeasurement	VIKINGv20111019	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingMapRemeasAver	VIKINGZYSELJv20160909	Merged Flag indicating most probable morphological classification -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingMapRemeasAver	VIKINGZYSELJv20170124	Merged Flag indicating most probable morphological classification -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingMapRemeasurement	VIKINGZYSELJv20160909	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vikingMapRemeasurement	VIKINGZYSELJv20170124	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCDR1	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCDR2	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCDR3	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCDR4	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCDR5	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20110816	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20110909	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20120126	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20121128	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20130304	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20130805	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20140428	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20140903	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20150309	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20151218	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20160311	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20160822	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20170109	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20170411	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20171101	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20180702	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20181120	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20191212	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20210708	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20230816	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcDetection	VMCv20240226	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcListRemeasurement	VMCv20110816	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcListRemeasurement	VMCv20110909	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcdeepDetection	VMCDEEPv20230713	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vmcdeepDetection	VMCDEEPv20240506	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vvvDetection	VVVDR1	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vvvDetection	VVVDR2	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vvvDetection	VVVv20100531	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 borderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vvvListRemeasurement	VVVv20100531	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class	vvvListRemeasurement	VVVv20110718	Flag indicating most probable morphological classification {catalogue TType keyword: Classification} -1 stellar, +1 non--stellar, 0 noise, -2 borderline stellar, -3 boderline galaxy, -9 saturated	smallint	2			src.class
			A discrete classification code for each detected image, based on various cuts on morphological parameters (see the Glossary entries for classStat and ell for further details). It is primarily based on cuts on the stellar profile statistic classStat and necessarily trades-off completeness against reliability between point-like (e.g. stellar) and extended (e.g. galaxy) images. The class assignments are as follows: Flag Meaning Generally satisfies -2 Probable star +2.0≤classStat≤+3.0 -1 Star -3.0≤classStat≤+2.0 0 Noise classStat≤-3.0 +1 Galaxy classStat≥+3.0 but these are also moderated by ellipticity overrides (e.g. ellipticity≥0.9 is flagged as class 0) and magnitude-dependent information (e.g. near or at saturation objects are preferentially flagged as class -1). Hence class is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely point-source samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point/extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser). For much more information concerning the class attribute, see the CASU standard source extraction documentation.
class_star	masterDR2	SKYMAPPER	Maximum stellarity index from photometry table (between 0=no star and 1=star)	real	4			src.class.starGalaxy;stat.mean
classification	igsl_source	GAIADR1	Classification, simply a 0=star and 1=nonstar taken from different sources as given in the souce_classification field	bit	1			meta.code.class
classification	variable_summary	GAIADR1	Photometric variability classification "RRLYR" (for RR Lyrae) or "CEP" (for Cepheid)	varchar	8			meta.code.class;src.var
classStat	sharksDetection	SHARKSv20210222	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	sharksDetection	SHARKSv20210421	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	ultravistaDetection	ULTRAVISTADR4	S-Extractor classification statistic CLASS_STAR (0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			CLASS_STAR is SE's classification of objects on the basis of a Neural Network Output. There are several input parameters which are directly linked to the CLASS_STAR parameter: PIXEL_SCALE Pixel size in arsec. SEEING_FWHM FWHM of stellar images in arcsec. BACK_SIZE Size, or Width, Height (in pixels) of a background mesh. THRESH_TYPE Meaning of the DETECT_THRESH and ANALYSIS_THRESH parameters: RELATIVE (scaling factor to the background rms) or ABSOLUTE (absolute level) ANALYSIS_THRESH Threshold (in surface brightness) at which CLASS_STAR and FWHM operate: 1argument (relative to background rms) or 2 arguments (mu[mag/arcsec²], zero-point [mag]) A perfectly point-like (stellar) object has class=1.0.
classStat	ultravistaMapRemeasAver	ULTRAVISTADR4	Averaged N(0,1) stellarness-of-profile statistic (SE: classification statistic CLASS_STAR; 0 - galaxy, 1 - star)	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	ultravistaMapRemeasurement	ULTRAVISTADR4	N(0,1) stellarness-of-profile statistic (SE: classification statistic CLASS_STAR; 0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSDR2	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSDR3	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSDR4	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSDR5	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSDR6	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20120926	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20130417	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20140409	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20150108	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20160114	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20160507	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20170630	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20180419	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20201209	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20231101	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection	VHSv20240731	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vhsDetection, vhsListRemeasurement	VHSDR1	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	videoDetection	VIDEODR2	S-Extractor classification statistic CLASS_STAR (0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			CLASS_STAR is SE's classification of objects on the basis of a Neural Network Output. There are several input parameters which are directly linked to the CLASS_STAR parameter: PIXEL_SCALE Pixel size in arsec. SEEING_FWHM FWHM of stellar images in arcsec. BACK_SIZE Size, or Width, Height (in pixels) of a background mesh. THRESH_TYPE Meaning of the DETECT_THRESH and ANALYSIS_THRESH parameters: RELATIVE (scaling factor to the background rms) or ABSOLUTE (absolute level) ANALYSIS_THRESH Threshold (in surface brightness) at which CLASS_STAR and FWHM operate: 1argument (relative to background rms) or 2 arguments (mu[mag/arcsec²], zero-point [mag]) A perfectly point-like (stellar) object has class=1.0.
classStat	videoDetection	VIDEODR3	S-Extractor classification statistic CLASS_STAR (0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			CLASS_STAR is SE's classification of objects on the basis of a Neural Network Output. There are several input parameters which are directly linked to the CLASS_STAR parameter: PIXEL_SCALE Pixel size in arsec. SEEING_FWHM FWHM of stellar images in arcsec. BACK_SIZE Size, or Width, Height (in pixels) of a background mesh. THRESH_TYPE Meaning of the DETECT_THRESH and ANALYSIS_THRESH parameters: RELATIVE (scaling factor to the background rms) or ABSOLUTE (absolute level) ANALYSIS_THRESH Threshold (in surface brightness) at which CLASS_STAR and FWHM operate: 1argument (relative to background rms) or 2 arguments (mu[mag/arcsec²], zero-point [mag]) A perfectly point-like (stellar) object has class=1.0.
classStat	videoDetection	VIDEODR4	S-Extractor classification statistic CLASS_STAR (0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			CLASS_STAR is SE's classification of objects on the basis of a Neural Network Output. There are several input parameters which are directly linked to the CLASS_STAR parameter: PIXEL_SCALE Pixel size in arsec. SEEING_FWHM FWHM of stellar images in arcsec. BACK_SIZE Size, or Width, Height (in pixels) of a background mesh. THRESH_TYPE Meaning of the DETECT_THRESH and ANALYSIS_THRESH parameters: RELATIVE (scaling factor to the background rms) or ABSOLUTE (absolute level) ANALYSIS_THRESH Threshold (in surface brightness) at which CLASS_STAR and FWHM operate: 1argument (relative to background rms) or 2 arguments (mu[mag/arcsec²], zero-point [mag]) A perfectly point-like (stellar) object has class=1.0.
classStat	videoDetection	VIDEODR5	S-Extractor classification statistic CLASS_STAR (0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			CLASS_STAR is SE's classification of objects on the basis of a Neural Network Output. There are several input parameters which are directly linked to the CLASS_STAR parameter: PIXEL_SCALE Pixel size in arsec. SEEING_FWHM FWHM of stellar images in arcsec. BACK_SIZE Size, or Width, Height (in pixels) of a background mesh. THRESH_TYPE Meaning of the DETECT_THRESH and ANALYSIS_THRESH parameters: RELATIVE (scaling factor to the background rms) or ABSOLUTE (absolute level) ANALYSIS_THRESH Threshold (in surface brightness) at which CLASS_STAR and FWHM operate: 1argument (relative to background rms) or 2 arguments (mu[mag/arcsec²], zero-point [mag]) A perfectly point-like (stellar) object has class=1.0.
classStat	videoDetection	VIDEOv20100513	S-Extractor classification statistic CLASS_STAR (0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			CLASS_STAR is SE's classification of objects on the basis of a Neural Network Output. There are several input parameters which are directly linked to the CLASS_STAR parameter: PIXEL_SCALE Pixel size in arsec. SEEING_FWHM FWHM of stellar images in arcsec. BACK_SIZE Size, or Width, Height (in pixels) of a background mesh. THRESH_TYPE Meaning of the DETECT_THRESH and ANALYSIS_THRESH parameters: RELATIVE (scaling factor to the background rms) or ABSOLUTE (absolute level) ANALYSIS_THRESH Threshold (in surface brightness) at which CLASS_STAR and FWHM operate: 1argument (relative to background rms) or 2 arguments (mu[mag/arcsec²], zero-point [mag]) A perfectly point-like (stellar) object has class=1.0.
classStat	videoDetection	VIDEOv20111208	S-Extractor classification statistic CLASS_STAR (0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			CLASS_STAR is SE's classification of objects on the basis of a Neural Network Output. There are several input parameters which are directly linked to the CLASS_STAR parameter: PIXEL_SCALE Pixel size in arsec. SEEING_FWHM FWHM of stellar images in arcsec. BACK_SIZE Size, or Width, Height (in pixels) of a background mesh. THRESH_TYPE Meaning of the DETECT_THRESH and ANALYSIS_THRESH parameters: RELATIVE (scaling factor to the background rms) or ABSOLUTE (absolute level) ANALYSIS_THRESH Threshold (in surface brightness) at which CLASS_STAR and FWHM operate: 1argument (relative to background rms) or 2 arguments (mu[mag/arcsec²], zero-point [mag]) A perfectly point-like (stellar) object has class=1.0.
classStat	videoListRemeasurement	VIDEOv20100513	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGDR2	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGDR3	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGDR4	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGv20111019	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGv20130417	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGv20140402	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGv20150421	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGv20151230	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGv20160406	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGv20161202	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection	VIKINGv20170715	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingDetection, vikingListRemeasurement	VIKINGv20110714	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingMapRemeasAver	VIKINGZYSELJv20160909	Averaged N(0,1) stellarness-of-profile statistic (SE: classification statistic CLASS_STAR; 0 - galaxy, 1 - star)	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingMapRemeasAver	VIKINGZYSELJv20170124	Averaged N(0,1) stellarness-of-profile statistic (SE: classification statistic CLASS_STAR; 0 - galaxy, 1 - star)	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingMapRemeasurement	VIKINGZYSELJv20160909	N(0,1) stellarness-of-profile statistic (SE: classification statistic CLASS_STAR; 0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vikingMapRemeasurement	VIKINGZYSELJv20170124	N(0,1) stellarness-of-profile statistic (SE: classification statistic CLASS_STAR; 0 - galaxy, 1 - star) {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCDR1	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCDR2	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCDR3	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCDR4	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCDR5	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20110909	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20120126	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20121128	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20130304	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20130805	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20140428	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20140903	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20150309	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20151218	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20160311	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20160822	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20170109	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20170411	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20171101	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20180702	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20181120	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20191212	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20210708	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20230816	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection	VMCv20240226	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcDetection, vmcListRemeasurement	VMCv20110816	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcdeepDetection	VMCDEEPv20230713	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vmcdeepDetection	VMCDEEPv20240506	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vvvDetection	VVVDR1	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vvvDetection	VVVDR2	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
classStat	vvvDetection, vvvListRemeasurement	VVVv20100531	N(0,1) stellarness-of-profile statistic {catalogue TType keyword: Statistic}	real	4			stat
			An equivalent N(0,1) measure of how point-like an image is, used in deriving the class attribute in a 'necessary but not sufficient' sense. Derived mainly from the curve-of-growth of flux using the well-defined stellar locus as a function of magnitude as a benchmark (see Irwin et al. 2004 SPIE 5493 411 for more details). Note that the assignment of the discrete classification flag class=-1 is based on a cut of -3.0≤classStat≤+2.0 cut on this statistic (along with ellipticity and magnitude information) and hence is conservative in the sense that it defines galaxy samples optimised for completeness, and conversely stellar samples optimised for reliability. For examples of the use of the various classification attributes in sample selection, especially with respect to the trade-off between completeness and reliability for point or extended samples, see the Views predefined in the WSA schema (these can be examined using the schema browser).
clat	twomass_sixx2_psc, twomass_sixx2_scn	TWOMASS	The computed dec (J2000 EQ) of scan center in sexigesimal.	varchar	16
clat	twomass_sixx2_xsc	TWOMASS	The computed dec (J2000 EQ) in sexigesimal.	varchar	16
cld	twomass_scn	TWOMASS	Special flag indicating whether or not a cloud was found in the scan after comparison of its photometry to that of overlapping scans in the database.	smallint	2			meta.code
cld	twomass_sixx2_scn	TWOMASS	downgrade from DB photom. overlap comparison (clouds) (0|1)	smallint	2
clon	twomass_sixx2_psc, twomass_sixx2_scn	TWOMASS	The computed ra (J2000 EQ) of scan center in sexigesimal.	varchar	16
clon	twomass_sixx2_xsc	TWOMASS	The computed ra (J2000 EQ) in sexigesimal.	varchar	16
closeFlag	sage_lmcIracSource	SPITZER	Close source flag	int	4
closeFlag	sage_smcIRACv1_5Source	SPITZER	Close source flag: 0 no sources in the Archive within 3" of the source; 1 sources in the Archive between 2.5" and 3" of the source; 2 sources in the Archive between 2.0" and 2.5" of the source; 3 sources in the Archive between 1.5" and 2.0" of the source; 4 sources in the Archive between 1.0" and 1.5" of the source; 5 sources in the Archive between 0.5" and 1.0" of the source; 6 sources in the Archive within 0.5" of the source.	int	4
closeFullFlag	sage_lmcIracSource	SPITZER	Close full source flag	int	4
closefullflag	sage_smcIRACv1_5Source	SPITZER	Close full source flag	int	4
CluStar_Flag	ravedr5Source	RAVE	0 => not a targeted cluster observation, 1 => targeted cluster observation	tinyint	1			meta.code.qual
cmag	smashdr2_source	SMASH	Calibrated magnitude version of MAG	real	4
cntr	allwise_sc	WISE	Unique identification number for this object in the AllWISE Catalog/Reject Table. This number is formed from the source_id, which is in turn formed from the coadd_id and source number, src.	bigint	8
			The cntr value is formed by making the source_id into an integer, in the format: RRRRsDDDtrevIIIIII, where RRRR = Tile center RA in deci-degrees, truncated not rounded (e.g. RRRR=int[10*ra]), as in the coadd_id. s = Tile center Declination sign translated from "p" or "m" into "1" or "0", respectively. DDD = Tile center Declination in deci-degrees, as in the coadd_id. For positive declinations, the tenths of a degree is truncated not rounded (e.g. DDD=int[10*dec]). For negative declinations, the tenths of a degree is always truncated leftward on the number line (e.g. DDD=ceil[10*abs(dec)]. [trev] = Disambiguation string, translated where necessary into digits corresponding to the letters' places in the alphabet. t - Tile type translated into two digits, zero-filled ("a" = "01"). r - Data Release identifier translated into a single digit, (For AllWISE: "c" = "3"). e - Survey Phase ("5" = AllWISE [Full cryogenic + 3-Band Cryo + Post-Cryo]). v - Processing version. The translated disambiguation string is always 01351 for AllWISE. IIIIII = six-digit, zero-filled, sequential extracted source number, src, within the Tile. For example, a source in the Tile 3041m137_ac51, with a source_id of '3041m137_ac51-012345', has a cntr value of 3041013701351012345. NOTE: AllWISE Catalog and Reject Table entries are cross-referenced with the Multiepoch Photometry (MEP) Database via the cntr identifier. The Catalog/Reject Table cntr value is referred to as cntr_mf in the MEP Database.
cntr	catwise_2020, catwise_prelim	WISE	Unique identifier	int	4
cntr	sage_smcIRACv1_5Source	SPITZER	entry counter (key) number (unique within table).	int	4
cntr	smashdr2_deep, smashdr2_object	SMASH	Unique identification number for the Catalog object	bigint	8
cntr	smashdr2_source	SMASH	Unique identification number for the Catalog source	bigint	8
cntr	twomass_sixx2_scn, twomass_sixx2_xsc	TWOMASS	entry counter (key) number (unique within table)	int	4
cntr	wise_allskysc	WISE	Unique identification number for the Catalog source This number is formed entirely from the source_id, which is in turn formed from the coadd_id and source number src. On average, sources with cntr values close to each other are also close to each other on the sky, except at Tile boundaries.	bigint	8			meta.id;meta.main
cntr	wise_prelimsc	WISE	Unique identification number for the Catalog source This number is formed entirely from the source_id, which is in turn formed from the coadd_id and source number src. On average, sources with cntr values close to each other are also close to each other on the sky, except at Tile boundaries	bigint	8			meta.id;meta.main
coadd	twomass_psc	TWOMASS	Sequence number of the Atlas Image in which the position of this source falls.	smallint	2			meta.number
coadd	twomass_xsc	TWOMASS	3-digit coadd number (unique within scan).	smallint	2			meta.number
coadd_id	allwise_sc	WISE	Atlas Tile identifier from which source was extracted.	varchar	20
			The identifier has the general form: RRRRsDDD_[trev], where RRRR = Tile center RA in deci-degrees, truncated not rounded (e.g. RRRR=int[10*ra]). s = Tile center Declination sign ("p"="+", "m"="-") DDD = Tile center Declination in deci-degrees. For positive declinations, the tenths of a degree is truncated not rounded (e.g. DDD=int[10*dec]). For negative declinations, the tenths of a degree is always truncated leftward on the number line (e.g. DDD=ceil[10*abs(dec)]. [trev] = Disambiguation string. t - Tile type ("a" = Atlas) r - Data Release ("c" = AllWISE) e - Survey Phase ("5" = AllWISE [Full Cryogenic + 3-Band Cryo + Post-Cryo]) v - Processing version. The disambiguation string is always ac51 for Tiles in the AllWISE Release products.
coadd_id	wise_allskysc	WISE	Atlas Tile identifier from which source was extracted.	char	20
coadd_id	wise_prelimsc	WISE	Atlas Tile identifier from which source was extracted	char	20
coadd_key	twomass_psc	TWOMASS	Unique identification number of the record in the Atlas Image Data Table (NOT YET AVAILABLE) that corresponds to Image in which the position of this source falls.	int	4			meta.id
coadd_key	twomass_xsc	TWOMASS	key to coadd data record in "scan DB".	int	4			meta.id
combicatID	MapProvenance	ULTRAVISTADR4	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combicatID	ultravistaMapAverageWeights	ULTRAVISTADR4	Unique identifier for combined average catalogue	bigint	8		-99999999	meta_id;meta_main
combiframeID	Provenance	SHARKSv20210222	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	SHARKSv20210421	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	ULTRAVISTADR4	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSDR1	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSDR2	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSDR3	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSDR4	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSDR5	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSDR6	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20120926	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20130417	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20140409	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20150108	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20160114	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20160507	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20170630	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20180419	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20201209	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20231101	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VHSv20240731	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIDEODR2	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIDEODR3	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIDEODR4	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIDEODR5	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIDEOv20100513	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIDEOv20111208	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGDR2	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGDR3	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGDR4	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20110714	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20111019	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20130417	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20140402	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20150421	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20151230	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20160406	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20161202	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VIKINGv20170715	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCDEEPv20230713	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCDEEPv20240506	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCDR1	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCDR2	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCDR3	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCDR4	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCDR5	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20110816	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20110909	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20120126	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20121128	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20130304	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20130805	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20140428	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20140903	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20150309	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20151218	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20160311	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20160822	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20170109	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20170411	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20171101	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20180702	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20181120	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20191212	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20210708	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20230816	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VMCv20240226	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VSAQC	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VVVDR1	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VVVDR2	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VVVDR5	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VVVXDR1	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VVVv20100531	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combiframeID	Provenance	VVVv20110718	the UID of the combined frame (=combiframe)	bigint	8			obs.field
combineTypeCode	CombinedFilters	SHARKSv20210222	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	SHARKSv20210421	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	ULTRAVISTADR4	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VHSv20201209	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VHSv20231101	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VHSv20240731	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VMCDEEPv20230713	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VMCDEEPv20240506	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VMCDR5	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VMCv20191212	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VMCv20210708	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VMCv20230816	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VMCv20240226	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VVVDR5	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combineTypeCode	CombinedFilters	VVVXDR1	Code for combineType: 1:1/varSq,	tinyint	1			meta.code
combiProdType	ProductLinks	SHARKSv20210222	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	SHARKSv20210421	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	ULTRAVISTADR4	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSDR1	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSDR2	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSDR3	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSDR4	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSDR5	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSDR6	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20120926	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20130417	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20150108	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20160114	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20160507	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20170630	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20180419	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20201209	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20231101	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VHSv20240731	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIDEODR2	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIDEODR3	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIDEODR4	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIDEODR5	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIDEOv20100513	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIDEOv20111208	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGDR2	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGDR3	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGDR4	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGv20110714	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGv20111019	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGv20130417	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGv20150421	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGv20151230	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGv20160406	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGv20161202	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VIKINGv20170715	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCDEEPv20230713	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCDEEPv20240506	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCDR1	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCDR3	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCDR4	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCDR5	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20110816	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20110909	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20120126	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20121128	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20130304	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20130805	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20140428	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20140903	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20150309	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20151218	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20160311	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20160822	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20170109	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20170411	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20171101	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20180702	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20181120	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20191212	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20210708	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20230816	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VMCv20240226	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VVVDR1	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VVVDR2	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VVVDR5	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VVVXDR1	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VVVv20100531	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProdType	ProductLinks	VVVv20110718	Type (stack,tile,mosaic) of combination frame	varchar	8			??
combiProductID	ProductLinks	SHARKSv20210222	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	SHARKSv20210421	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	ULTRAVISTADR4	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSDR1	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSDR2	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSDR3	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSDR4	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSDR5	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSDR6	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20120926	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20130417	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20150108	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20160114	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20160507	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20170630	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20180419	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20201209	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20231101	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VHSv20240731	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIDEODR2	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIDEODR3	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIDEODR4	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIDEODR5	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIDEOv20100513	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIDEOv20111208	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGDR2	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGDR3	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGDR4	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGv20110714	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGv20111019	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGv20130417	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGv20150421	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGv20151230	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGv20160406	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGv20161202	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VIKINGv20170715	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCDEEPv20230713	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCDEEPv20240506	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCDR1	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCDR3	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCDR4	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCDR5	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20110816	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20110909	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20120126	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20121128	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20130304	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20130805	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20140428	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20140903	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20150309	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20151218	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20160311	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20160822	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20170109	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20170411	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20171101	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20180702	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20181120	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20191212	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20210708	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20230816	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VMCv20240226	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VVVDR1	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VVVDR2	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VVVDR5	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VVVXDR1	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VVVv20100531	Product ID of combination frame	int	4			??
combiProductID	ProductLinks	VVVv20110718	Product ID of combination frame	int	4			??
COMMENT	denisi, denisj	SIXDF	supplied comments	varchar	48
COMMENT	fsc	SIXDF	supplied comments	char	25
COMMENT	rass	SIXDF	supplied comments	varchar	17
COMMENT	supercos	SIXDF	SuperCOSMOS reference (field no. + B_index + R_index)	char	17
comment	ArchiveCurationHistory	SHARKSv20210222	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	SHARKSv20210421	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	ULTRAVISTADR4	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSDR1	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSDR2	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSDR3	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSDR4	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSDR5	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSDR6	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20120926	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20130417	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20150108	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20160114	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20160507	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20170630	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20180419	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20201209	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20231101	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VHSv20240731	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIDEODR2	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIDEODR3	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIDEODR4	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIDEODR5	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIDEOv20100513	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIDEOv20111208	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGDR2	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGDR3	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGDR4	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGv20110714	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGv20111019	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGv20130417	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGv20150421	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGv20151230	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGv20160406	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGv20161202	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VIKINGv20170715	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCDEEPv20230713	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCDEEPv20240506	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCDR1	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCDR3	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCDR4	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCDR5	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20110816	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20110909	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20120126	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20121128	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20130304	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20130805	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20140428	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20140903	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20150309	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20151218	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20160311	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20160822	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20170109	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20170411	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20171101	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20180702	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20181120	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20191212	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20210708	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20230816	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VMCv20240226	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VVVDR1	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VVVDR2	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VVVDR5	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VVVXDR1	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VVVv20100531	Comment string supplied by the curator	varchar	256			??
comment	ArchiveCurationHistory	VVVv20110718	Comment string supplied by the curator	varchar	256			??
compFile	MultiframeDetector	SHARKSv20210222	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	SHARKSv20210421	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	ULTRAVISTADR4	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSDR1	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSDR2	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSDR3	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSDR4	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSDR5	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSDR6	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20120926	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20130417	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20140409	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20150108	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20160114	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20160507	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20170630	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20180419	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20201209	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20231101	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VHSv20240731	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIDEODR2	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIDEODR3	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIDEODR4	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIDEODR5	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIDEOv20100513	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIDEOv20111208	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGDR2	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGDR3	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGDR4	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20110714	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20111019	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20130417	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20140402	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20150421	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20151230	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20160406	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20161202	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VIKINGv20170715	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCDEEPv20230713	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCDEEPv20240506	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCDR1	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCDR2	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCDR3	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCDR4	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCDR5	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20110816	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20110909	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20120126	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20121128	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20130304	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20130805	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20140428	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20140903	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20150309	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20151218	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20160311	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20160822	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20170109	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20170411	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20171101	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20180702	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20181120	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20191212	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20210708	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20230816	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VMCv20240226	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VVVDR1	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VVVDR2	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VVVDR5	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VVVXDR1	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VVVv20100531	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	MultiframeDetector	VVVv20110718	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
compFile	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	Filename of the compressed image of this image frame, eg. server:/path/filename.jpg	varchar	256		NONE	meta.id;meta.file
conf	glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca	GLIMPSE	Close source flag	smallint	2		-9	meta.code
conf	glimpse_hrc_inter, glimpse_mca_inter	GLIMPSE	Confusion flag	smallint	2		-9	meta.code
conf160	sage_lmcMips160Source	SPITZER	Confusion Flag for band 160, currently unused	int	4
conf24	sage_lmcMips24Source	SPITZER	Confusion Flag for band 24, currently unused	int	4
conf70	sage_lmcMips70Source	SPITZER	Confusion Flag for band 70, currently unused	int	4
confID	Multiframe	SHARKSv20210222	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	SHARKSv20210421	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	ULTRAVISTADR4	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSDR1	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSDR2	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSDR3	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSDR4	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSDR5	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSDR6	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20120926	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20130417	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20140409	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20150108	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20160114	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20160507	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20170630	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20180419	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20201209	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20231101	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VHSv20240731	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIDEODR2	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIDEODR3	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIDEODR4	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIDEODR5	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIDEOv20100513	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIDEOv20111208	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIKINGDR2	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIKINGDR3	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIKINGDR4	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIKINGv20110714	UID of library calibration confidence frame {image extension keyword: CIR_CPM}	bigint	8		-99999999	obs.field
confID	Multiframe	VIKINGv20111019