Table information for 'gdr2mock.main'

General

Table Description: A synthetic Milky Way catalog mimicking GDR2 in stellar content and data model.

This table is available for ADQL queries and through the TAP endpoint.

Resource Description: This catalogue is a simulation of the Gaia DR2 stellar content using Galaxia (a tool to sample stars from a Besancon-like Milky Way model), 3d dust extinction maps and the latest PARSEC Isochrones. It is mimicking the Gaia DR2 data model and an apparent magnitude limit of g=20,7. Extinctions and photometry in different bands have also been included in a supplementary table as well as uncertainty estimates using a scaled nominal error model.

For a list of all services and tables belonging to this table's resource, see Information on resource 'A Gaia DR2 mock stellar catalog'

Further information

Query Validator

A web form for validating queries against the Gaia DR2 data model without actually submitting them is available at http://gaia.ari.uni-heidelberg.de/adql-validator.html

Associate Photometry

To add simulated multi-band photometry, join gdr2mock.main with gdr2mock.photometry USING (index_parsec).

Nominal error model

The nominal error model for the astrometry is way too optimistic for stars with an apparent G magnitude smaller than 12. For stars in this range a factor of approximately five needs to be added to the reported values. For details refer to https://doi.org/10.1051/0004-6361/201832727. For the photometry the nominal error model seems to be too conservative and the GDR2 photometry seems to be more precise (cf. https://doi.org/10.1051/0004-6361/201832756).

Coping with the table size

gdr2mock.main is a large table. A sequential scan of it will take about an hour on a machine not otherwise loaded. Therefore, only do queries on the whole table with tested queries that actually yield what you expect them to yield. For development, please restrict yourself to reasonably sized-subsets. The recommended pattern is to use common table expressions (CTEs) like this:

WITH sample AS (
        SELECT * FROM gdr2mock.main
        WHERE distance(ra, dec, 66.73, 75.87)<2)
SELECT ra, dec, 1/parallax
FROM sample
WHERE age BETWEEN 0.2 and 0.3

When extending this kind of query to the whole sky (or other regions), all you need to do is edit the statement after WITH.

Note that CTEs are not yet available on all TAP services. On services that do not have them, you can substitute them with subqueries in many situations.

Preferred form of spatial constraints

Please note that the standard (and fast) pattern for spatial selections is cone-like; you can equivalently write:

WHERE distance(ra, dec, 66.73, 75.87)<2

or:

WHERE 1=CONTAINS(
        POINT(ra, dec),
        CIRCLE(66.73, 75.87, 2))

Both assume ADQL 2.1; in ADQL 2.0 (which still runs on the major Gaia services in early 2018; this form also runs on ADQL 2.1), it's:

WHERE 1=CONTAINS(
        POINT('', ra, dec),
        CIRCLE('', 66.73, 75.87, 2))

Do not use “rectangular” selection ("ra between 1 and 2" or "ra<=2 and ra>=1") – this will be slow and may introduce all kinds of weird effects depending on the coordinate frame.

Chunking the catalog

In some situations, you want to exhaustively partition the catalog. To do that, use the source_id column, which has a healpix number in the upper 29 bits. The following partition will yield chunks of no more than 10 million objects and was used to produce the dump of the catalog:

limits = [0, 164291313590476000L, 191269676664280480L,
        207871746248160160L, 248685167308531040L, 271513012986365600L,
        345530573214495872L, 411247098650686208L, 431307932731207552L,
        458978048704340352L, 474888365388891648L, 510167763254636032L,
        523311068341874048L, 539913137925753728L, 674113200395447936L,
        936287549240881152L, 1060803071119978752L, 1276629975710414592L,
        1473087799119657216L, 1738720912461732096L, 1791985885710012928L,
        1810663213991877376L, 1820347754582473984L, 1823806519079115520L,
        1826573530676428800L, 1832107553871055360L, 1843175600260308480L,
        1860469422743516416L, 1869462210434784768L, 1920651924985080320L,
        1961465346045451264L, 1974608651132689408L, 1990518967817240832L,
        2005737531602463744L, 2017497330891045120L, 2020956095387686656L,
        2023723106985000192L, 2025798365682984960L, 2029948883078955008L,
        2032024141776939776L, 2034099400474924800L, 2038249917870894848L,
        2045859199763506432L, 2050701470058804480L, 2059002504850744320L,
        2064536528045370880L, 2072145809937982464L, 2075604574434624000L,
        2083905609226563840L, 2103966443307085056L, 2152389146260067584L,
        2168991215843947264L, 2177984003535215360L, 2185593285427826944L,
        2199428343414393344L, 2207729378206333184L, 2222256189092227840L,
        2270678892045210368L, 2544613040179225088L, 2768049226662272512L,
        2918851358715846656L, 2931994663803084288L, 2948596733386963968L,
        3006703976930543104L, 3035757598702332416L, 3052359668286212096L,
        3099398865440538112L, 3112542170527775744L, 3128452487212327424L,
        3165115390876728320L, 3316609275829630464L, 3331827839614853632L,
        3354655685292687872L, 3383017554165149184L, 3422447469426863104L,
        3442508303507384832L, 3472945431077830656L, 3672862018983715328L,
        4026347750540486656L, 4036032291131083264L, 4036724044030411776L,
        4037415796929739776L, 4038107549829068288L, 4038799302728396288L,
        4039491055627724800L, 4040182808527053312L, 4040874561426381312L,
        4041566314325709824L, 4042258067225037824L, 4042949820124366336L,
        4043641573023694848L, 4044333325923022848L, 4045025078822351360L,
        4045716831721679872L, 4046408584621007872L, 4047792090419664896L,
        4048483843318992896L, 4049175596218321408L, 4049867349117649408L,
        4050559102016977920L, 4051250854916306432L, 4051942607815634432L,
        4052634360714962944L, 4053326113614290944L, 4055401372312275968L,
        4056093125211604480L, 4057476631010260992L, 4058860136808918016L,
        4059551889708246016L, 4060935395506902528L, 4061627148406231040L,
        4062318901305559552L, 4063010654204887552L, 4063702407104216064L,
        4064394160003544064L, 4065085912902872576L, 4066469418701529088L,
        4067852924500186112L, 4068544677399514112L, 4070619936097499136L,
        4072695194795484160L, 4075462206392797184L, 4076153959292125696L,
        4076845712191454208L, 4077537465090782208L, 4079612723788767232L,
        4084454994084065792L, 4087913758580707328L, 4089297264379363840L,
        4089989017278692352L, 4090680770178020352L, 4092064275976677376L,
        4093447781775333888L, 4095523040473318912L, 4096906546271975424L,
        4098981804969960448L, 4101057063667945472L, 4103132322365930496L,
        4103824075265258496L, 4105207581063915008L, 4106591086862572032L,
        4107282839761900032L, 4108666345560556544L, 4110049851359213568L,
        4112125110057198592L, 4115583874553840128L, 4116275627453168128L,
        4116967380352496640L, 4118350886151153152L, 4119734391949809664L,
        4122501403547123200L, 4124576662245108224L, 4130802438339062784L,
        4137719967332346368L, 4143945743426300928L, 4146021002124285952L,
        4149479766620927488L, 4153630284016897536L, 4157089048513539072L,
        4161239565909509120L, 4170924106500105728L, 4181300399990030336L,
        4193751952177939968L, 4200669481171223552L, 4202744739869208576L,
        4204819998567193088L, 4210354021761819648L, 4221422068151072768L,
        4241482902231593984L, 4251167442822190592L, 4251859195721519104L,
        4253242701520175616L, 4254626207318832128L, 4257393218916145664L,
        4260851983412787200L, 4262927242110772224L, 4265002500808757248L,
        4267769512406070272L, 4273995288500025344L, 4280912817493308416L,
        4286446840687934976L, 4291289110983233536L, 4294056122580546560L,
        4298206639976516608L, 4303740663171143168L, 4309274686365769728L,
        4312733450862411264L, 4314808709560396288L, 4317575721157709312L,
        4321034485654350848L, 4338328308137559040L, 4369457188607333376L,
        4394360292983153152L, 4468377853211283456L, 4478062393801879552L,
        4487746934392476160L, 4504349003976355840L, 4509883027170982400L,
        4514033544566952448L, 4518875814862250496L, 4531327367050160128L,
        4567298517815233024L, 4631631537452766208L, 4828089360862009344L,
        5188492621412064256L, 5227922536673778688L, 5235531818566389760L,
        5238298830163703808L, 5241065841761016832L, 5248675123653627904L,
        5252133888150269952L, 5254209146848254976L, 5256284405546240000L,
        5271502969331463168L, 5299864838203923456L, 5305398861398550528L,
        5308857625895191552L, 5310932884593176576L, 5317158660687131648L,
        5323384436781086720L, 5330993718673697792L, 5335144236069667840L,
        5338603000566309888L, 5342061765062950912L, 5349671046955563008L,
        5355896823049518080L, 5367656622338099200L, 5403627773103171584L,
        5413312313693768704L, 5435448406472274944L, 5504623696405106688L,
        5518458754391672832L, 5525376283384955904L, 5533677318176896000L,
        5540594847170179072L, 5559272175452044288L, 5589017550123161600L,
        5597318584915101696L, 5604927866807712768L, 5613228901599652864L,
        5620838183492263936L, 5645049534968755200L, 5699006261116364800L,
        5725984624190169088L, 5786167126431732736L, 5799310431518970880L,
        5815220748203522048L, 5823521782995462144L, 5826980547492103168L,
        5831131064888073216L, 5833206323586058240L, 5834589829384715264L,
        5835281582284043264L, 5837356840982028288L, 5844966122874640384L,
        5849116640270609408L, 5851191898968594432L, 5852575404767251456L,
        5856034169263893504L, 5857417675062549504L, 5858801180861206528L,
        5860184686659863552L, 5862259945357847552L, 5865718709854489600L,
        5869869227250459648L, 5872636238847772672L, 5874711497545757696L,
        5876095003344414720L, 5880245520740384768L, 5882320779438369792L,
        5886471296834338816L, 5888546555532323840L, 5891313567129637888L,
        5894772331626278912L, 5899614601921576960L, 5904456872216876032L,
        5914141412807472128L, 5920367188901427200L, 5924517706297397248L,
        5927284717894710272L, 5929359976592695296L, 5930743482391351296L,
        5931435235290680320L, 5932126988190008320L, 5932818741089336320L,
        5934202246887993344L, 5936277505585978368L, 5937661011384634368L,
        5939736270082619392L, 5942503281679933440L, 5945962046176574464L,
        5949420810673216512L, 5951496069371201536L, 5953571328069186560L,
        5956338339666499584L, 5957721845465156608L, 5959105351263812608L,
        5960488857062469632L, 5962564115760454656L, 5965331127357767680L,
        5968098138955080704L, 5970173397653065728L, 5972248656351050752L,
        5975707420847692800L, 5977782679545677824L, 5979166185344333824L,
        5980549691142990848L, 5984700208538960896L, 5987467220136273920L,
        5990925984632915968L, 5995076502028884992L, 6001302278122840064L,
        6010986818713437184L, 6019287853505376256L, 6024821876700003328L,
        6027588888297316352L, 6028972394095973376L, 6031047652793958400L,
        6034506417290599424L, 6044190957881196544L, 6053183745572464640L,
        6057334262968434688L, 6061484780364403712L, 6067018803559030784L,
        6075319838350970880L, 6090538402136194048L, 6107140471720073216L,
        6137577599290519552L, 6199143607330740224L, 6233731252297155584L,
        6264168379867601920L, 6363089044471552000L, 6439873616296994816L,
        6581682960659300352L, 6648091238994819072L, 6664693308578699264L,
        6702047965142428672L, 6709657247035039744L, 6720033540524964864L,
        6724184057920934912L, 6726951069518247936L, 6730409834014889984L,
        6733868598511531008L, 6736635610108844032L, 6754621185491381248L,
        6762230467383992320L, 6774682019571902464L, 6863226390685927424L,
        6906806823343610880L, 6917183116833535999L]

Please don't use it to dump the catalog yourself; it might still come in handy if you filter out a large part of the objects but still have more than 10 million matches. Just regularly remove separation points.

The way to use it in python would be to say:

# make svc a TapService object with the right access URL, and then:
for low, high in zip(limits[:-1], limits[1:]):
        partial = svc.run_sync("WITH sample AS ("
                " SELECT * FROM gdr2mock.main"
                " WHERE source_id BETWEEN {} AND {})"
                " <your query against sample here>".format(low, high-1))

RVS magnitudes

In particular to aid in designing studies involving radial velocities, we give a column phot_rvs_mean_mag based on the approximations given in 2018arXiv180409365G. This is going to be severely off outside of 0.1&lt;BP-G&lt;1.7 (which only affects less than 50 ppm of the objects).

Dump of the catalog

While we strongly recommend that you try to structure your problem so as to make it work through ADQL, for the benefit of those who want to mirror the table or use it in services of their own, we provide a dump of the data in the form of FITS binary tables files; warning: pulling it transfers about 300 gigabytes, and dealing with this data volume is nontrivial.

Having said that: The dump files are available from http://dc.zah.uni-heidelberg.de/gdr2mock/q/download.

Citing this table

This table has an associated publication. If you use data from it, it may be appropriate to reference 2018PASP..130g4101R (ADS BibTeX entry for the publication) either in addition to or instead of the service reference.

To cite the table as such, we suggest the following BibTeX entry:

@MISC{vo:gdr2mock_main,
  year=2018,
  title={A Gaia DR2 mock stellar catalog},
  author={Rybizki, J. and Demleitner, M. and Fouesneau, M. and Bailer-Jones, C. and Rix, H.-W. and Andrae, R.},
  url={http://dc.zah.uni-heidelberg.de/tableinfo/gdr2mock.main},
  howpublished={{VO} resource provided by the {GAVO} Data Center},
  doi = {10.21938/SYx.JVejuu66uzDiLcfRlA}
}

Table Documentation

Query Validator

A web form for validating queries against the Gaia DR2 data model without actually submitting them is available at http://gaia.ari.uni-heidelberg.de/adql-validator.html

Associate Photometry

To add simulated multi-band photometry, join gdr2mock.main with gdr2mock.photometry USING (index_parsec).

Nominal error model

The nominal error model for the astrometry is way too optimistic for stars with an apparent G magnitude smaller than 12. For stars in this range a factor of approximately five needs to be added to the reported values. For details refer to https://doi.org/10.1051/0004-6361/201832727. For the photometry the nominal error model seems to be too conservative and the GDR2 photometry seems to be more precise (cf. https://doi.org/10.1051/0004-6361/201832756).

Coping with the table size

gdr2mock.main is a large table. A sequential scan of it will take about an hour on a machine not otherwise loaded. Therefore, only do queries on the whole table with tested queries that actually yield what you expect them to yield. For development, please restrict yourself to reasonably sized-subsets. The recommended pattern is to use common table expressions (CTEs) like this:

WITH sample AS (
        SELECT * FROM gdr2mock.main
        WHERE distance(ra, dec, 66.73, 75.87)<2)
SELECT ra, dec, 1/parallax
FROM sample
WHERE age BETWEEN 0.2 and 0.3

When extending this kind of query to the whole sky (or other regions), all you need to do is edit the statement after WITH.

Note that CTEs are not yet available on all TAP services. On services that do not have them, you can substitute them with subqueries in many situations.

Preferred form of spatial constraints

Please note that the standard (and fast) pattern for spatial selections is cone-like; you can equivalently write:

WHERE distance(ra, dec, 66.73, 75.87)<2

or:

WHERE 1=CONTAINS(
        POINT(ra, dec),
        CIRCLE(66.73, 75.87, 2))

Both assume ADQL 2.1; in ADQL 2.0 (which still runs on the major Gaia services in early 2018; this form also runs on ADQL 2.1), it's:

WHERE 1=CONTAINS(
        POINT('', ra, dec),
        CIRCLE('', 66.73, 75.87, 2))

Do not use “rectangular” selection ("ra between 1 and 2" or "ra<=2 and ra>=1") – this will be slow and may introduce all kinds of weird effects depending on the coordinate frame.

Chunking the catalog

In some situations, you want to exhaustively partition the catalog. To do that, use the source_id column, which has a healpix number in the upper 29 bits. The following partition will yield chunks of no more than 10 million objects and was used to produce the dump of the catalog:

limits = [0, 164291313590476000L, 191269676664280480L,
        207871746248160160L, 248685167308531040L, 271513012986365600L,
        345530573214495872L, 411247098650686208L, 431307932731207552L,
        458978048704340352L, 474888365388891648L, 510167763254636032L,
        523311068341874048L, 539913137925753728L, 674113200395447936L,
        936287549240881152L, 1060803071119978752L, 1276629975710414592L,
        1473087799119657216L, 1738720912461732096L, 1791985885710012928L,
        1810663213991877376L, 1820347754582473984L, 1823806519079115520L,
        1826573530676428800L, 1832107553871055360L, 1843175600260308480L,
        1860469422743516416L, 1869462210434784768L, 1920651924985080320L,
        1961465346045451264L, 1974608651132689408L, 1990518967817240832L,
        2005737531602463744L, 2017497330891045120L, 2020956095387686656L,
        2023723106985000192L, 2025798365682984960L, 2029948883078955008L,
        2032024141776939776L, 2034099400474924800L, 2038249917870894848L,
        2045859199763506432L, 2050701470058804480L, 2059002504850744320L,
        2064536528045370880L, 2072145809937982464L, 2075604574434624000L,
        2083905609226563840L, 2103966443307085056L, 2152389146260067584L,
        2168991215843947264L, 2177984003535215360L, 2185593285427826944L,
        2199428343414393344L, 2207729378206333184L, 2222256189092227840L,
        2270678892045210368L, 2544613040179225088L, 2768049226662272512L,
        2918851358715846656L, 2931994663803084288L, 2948596733386963968L,
        3006703976930543104L, 3035757598702332416L, 3052359668286212096L,
        3099398865440538112L, 3112542170527775744L, 3128452487212327424L,
        3165115390876728320L, 3316609275829630464L, 3331827839614853632L,
        3354655685292687872L, 3383017554165149184L, 3422447469426863104L,
        3442508303507384832L, 3472945431077830656L, 3672862018983715328L,
        4026347750540486656L, 4036032291131083264L, 4036724044030411776L,
        4037415796929739776L, 4038107549829068288L, 4038799302728396288L,
        4039491055627724800L, 4040182808527053312L, 4040874561426381312L,
        4041566314325709824L, 4042258067225037824L, 4042949820124366336L,
        4043641573023694848L, 4044333325923022848L, 4045025078822351360L,
        4045716831721679872L, 4046408584621007872L, 4047792090419664896L,
        4048483843318992896L, 4049175596218321408L, 4049867349117649408L,
        4050559102016977920L, 4051250854916306432L, 4051942607815634432L,
        4052634360714962944L, 4053326113614290944L, 4055401372312275968L,
        4056093125211604480L, 4057476631010260992L, 4058860136808918016L,
        4059551889708246016L, 4060935395506902528L, 4061627148406231040L,
        4062318901305559552L, 4063010654204887552L, 4063702407104216064L,
        4064394160003544064L, 4065085912902872576L, 4066469418701529088L,
        4067852924500186112L, 4068544677399514112L, 4070619936097499136L,
        4072695194795484160L, 4075462206392797184L, 4076153959292125696L,
        4076845712191454208L, 4077537465090782208L, 4079612723788767232L,
        4084454994084065792L, 4087913758580707328L, 4089297264379363840L,
        4089989017278692352L, 4090680770178020352L, 4092064275976677376L,
        4093447781775333888L, 4095523040473318912L, 4096906546271975424L,
        4098981804969960448L, 4101057063667945472L, 4103132322365930496L,
        4103824075265258496L, 4105207581063915008L, 4106591086862572032L,
        4107282839761900032L, 4108666345560556544L, 4110049851359213568L,
        4112125110057198592L, 4115583874553840128L, 4116275627453168128L,
        4116967380352496640L, 4118350886151153152L, 4119734391949809664L,
        4122501403547123200L, 4124576662245108224L, 4130802438339062784L,
        4137719967332346368L, 4143945743426300928L, 4146021002124285952L,
        4149479766620927488L, 4153630284016897536L, 4157089048513539072L,
        4161239565909509120L, 4170924106500105728L, 4181300399990030336L,
        4193751952177939968L, 4200669481171223552L, 4202744739869208576L,
        4204819998567193088L, 4210354021761819648L, 4221422068151072768L,
        4241482902231593984L, 4251167442822190592L, 4251859195721519104L,
        4253242701520175616L, 4254626207318832128L, 4257393218916145664L,
        4260851983412787200L, 4262927242110772224L, 4265002500808757248L,
        4267769512406070272L, 4273995288500025344L, 4280912817493308416L,
        4286446840687934976L, 4291289110983233536L, 4294056122580546560L,
        4298206639976516608L, 4303740663171143168L, 4309274686365769728L,
        4312733450862411264L, 4314808709560396288L, 4317575721157709312L,
        4321034485654350848L, 4338328308137559040L, 4369457188607333376L,
        4394360292983153152L, 4468377853211283456L, 4478062393801879552L,
        4487746934392476160L, 4504349003976355840L, 4509883027170982400L,
        4514033544566952448L, 4518875814862250496L, 4531327367050160128L,
        4567298517815233024L, 4631631537452766208L, 4828089360862009344L,
        5188492621412064256L, 5227922536673778688L, 5235531818566389760L,
        5238298830163703808L, 5241065841761016832L, 5248675123653627904L,
        5252133888150269952L, 5254209146848254976L, 5256284405546240000L,
        5271502969331463168L, 5299864838203923456L, 5305398861398550528L,
        5308857625895191552L, 5310932884593176576L, 5317158660687131648L,
        5323384436781086720L, 5330993718673697792L, 5335144236069667840L,
        5338603000566309888L, 5342061765062950912L, 5349671046955563008L,
        5355896823049518080L, 5367656622338099200L, 5403627773103171584L,
        5413312313693768704L, 5435448406472274944L, 5504623696405106688L,
        5518458754391672832L, 5525376283384955904L, 5533677318176896000L,
        5540594847170179072L, 5559272175452044288L, 5589017550123161600L,
        5597318584915101696L, 5604927866807712768L, 5613228901599652864L,
        5620838183492263936L, 5645049534968755200L, 5699006261116364800L,
        5725984624190169088L, 5786167126431732736L, 5799310431518970880L,
        5815220748203522048L, 5823521782995462144L, 5826980547492103168L,
        5831131064888073216L, 5833206323586058240L, 5834589829384715264L,
        5835281582284043264L, 5837356840982028288L, 5844966122874640384L,
        5849116640270609408L, 5851191898968594432L, 5852575404767251456L,
        5856034169263893504L, 5857417675062549504L, 5858801180861206528L,
        5860184686659863552L, 5862259945357847552L, 5865718709854489600L,
        5869869227250459648L, 5872636238847772672L, 5874711497545757696L,
        5876095003344414720L, 5880245520740384768L, 5882320779438369792L,
        5886471296834338816L, 5888546555532323840L, 5891313567129637888L,
        5894772331626278912L, 5899614601921576960L, 5904456872216876032L,
        5914141412807472128L, 5920367188901427200L, 5924517706297397248L,
        5927284717894710272L, 5929359976592695296L, 5930743482391351296L,
        5931435235290680320L, 5932126988190008320L, 5932818741089336320L,
        5934202246887993344L, 5936277505585978368L, 5937661011384634368L,
        5939736270082619392L, 5942503281679933440L, 5945962046176574464L,
        5949420810673216512L, 5951496069371201536L, 5953571328069186560L,
        5956338339666499584L, 5957721845465156608L, 5959105351263812608L,
        5960488857062469632L, 5962564115760454656L, 5965331127357767680L,
        5968098138955080704L, 5970173397653065728L, 5972248656351050752L,
        5975707420847692800L, 5977782679545677824L, 5979166185344333824L,
        5980549691142990848L, 5984700208538960896L, 5987467220136273920L,
        5990925984632915968L, 5995076502028884992L, 6001302278122840064L,
        6010986818713437184L, 6019287853505376256L, 6024821876700003328L,
        6027588888297316352L, 6028972394095973376L, 6031047652793958400L,
        6034506417290599424L, 6044190957881196544L, 6053183745572464640L,
        6057334262968434688L, 6061484780364403712L, 6067018803559030784L,
        6075319838350970880L, 6090538402136194048L, 6107140471720073216L,
        6137577599290519552L, 6199143607330740224L, 6233731252297155584L,
        6264168379867601920L, 6363089044471552000L, 6439873616296994816L,
        6581682960659300352L, 6648091238994819072L, 6664693308578699264L,
        6702047965142428672L, 6709657247035039744L, 6720033540524964864L,
        6724184057920934912L, 6726951069518247936L, 6730409834014889984L,
        6733868598511531008L, 6736635610108844032L, 6754621185491381248L,
        6762230467383992320L, 6774682019571902464L, 6863226390685927424L,
        6906806823343610880L, 6917183116833535999L]

Please don't use it to dump the catalog yourself; it might still come in handy if you filter out a large part of the objects but still have more than 10 million matches. Just regularly remove separation points.

The way to use it in python would be to say:

# make svc a TapService object with the right access URL, and then:
for low, high in zip(limits[:-1], limits[1:]):
        partial = svc.run_sync("WITH sample AS ("
                " SELECT * FROM gdr2mock.main"
                " WHERE source_id BETWEEN {} AND {})"
                " <your query against sample here>".format(low, high-1))

RVS magnitudes

In particular to aid in designing studies involving radial velocities, we give a column phot_rvs_mean_mag based on the approximations given in 2018arXiv180409365G. This is going to be severely off outside of 0.1&lt;BP-G&lt;1.7 (which only affects less than 50 ppm of the objects).

Dump of the catalog

While we strongly recommend that you try to structure your problem so as to make it work through ADQL, for the benefit of those who want to mirror the table or use it in services of their own, we provide a dump of the data in the form of FITS binary tables files; warning: pulling it transfers about 300 gigabytes, and dealing with this data volume is nontrivial.

Having said that: The dump files are available from http://dc.zah.uni-heidelberg.de/gdr2mock/q/download.

Fields

Sorted by DB column index. [Sort alphabetically]

NameTable Head DescriptionUnitUCD
source_id ID Healpix number using Nside = 4096 with the nested scheme on equatorial coordinates times 2^35. The last digits of the source_id are reserved for a running number that serves as a unique identifier per HEALPix cell. This is formed in accordance with Gaia's source_id definition, but of course mock objects have no relation to any Gaia objects that may have an identical source_id. N/A meta.id;meta.main
ra RA (ICRS) Barycentric Right Ascension in ICRS at ref_epoch deg pos.eq.ra;meta.main
dec Dec (ICRS) Barycentric Declination in ICRS at ref_epoch deg pos.eq.dec;meta.main
ra_error Err. RA Standard error of ra (with cos δ applied). mas stat.error;pos.eq.ra
dec_error Err. Dec Standard error of dec mas stat.error;pos.eq.dec
pmra µ(RA) Proper motion in right ascension of the source in ICRS at ref_epoch. This is the projection of the proper motion vector in the direction of increasing right ascension. mas/yr pos.pm;pos.eq.ra
pmdec µ(Dec) Proper motion in declination at ref_epoch. mas/yr pos.pm;pos.eq.dec
pmra_error Err. PM(RA) Standard error of pmra mas/yr stat.error;pos.pm;pos.eq.ra
pmdec_error Err. PM(Dec) Standard error of pmdec mas/yr stat.error;pos.pm;pos.eq.dec
parallax Parallax Absolute barycentric stellar parallax of the source at the reference epoch ref_epoch. If looking for a distance, consider joining with gdr2dist.main and using the distances from there. mas pos.parallax
parallax_error Parallax_error Standard error of parallax mas stat.error;pos.parallax
phot_g_mean_mag m_G Mean magnitude in the G band. This is computed from the G-band mean flux applying the magnitude zero-point in the Vega scale. mag phot.mag;em.opt;stat.mean
phot_g_mean_flux Flux_G G-band mean flux as electrons per second. s**-1 phot.flux;em.opt;stat.mean
phot_g_mean_flux_error Err. Flux(G) Error on phot_g_mean_flux s**-1 stat.error;phot.flux;em.opt;stat.mean
phot_rp_mean_flux Flux RP Mean flux in the integrated RP band. s**-1 phot.flux;em.opt.R
phot_rp_mean_flux_error Err. Fl. RP Error in the mean flux in the integrated RP band. Errors are computed from the dispersion about the weighted mean of the input calibrated photometry. s**-1 stat.error;phot.flux;em.opt.R
phot_rp_mean_mag Mag RP Mean magnitude in the integrated RP band. This is computed from the RP-band mean flux applying the magnitude zero-point in the Vega scale. No error is provided for this quantity as the error distribution is only symmetric in flux space. For errors small compared to the flux (less than 10%, say), the magnitude error is well approximated by 1.09*flux/flux_err. mag phot.mag;em.opt.R
phot_bp_mean_flux Flux BP Mean flux in the integrated BP band. s**-1 phot.flux;em.opt.B
phot_bp_mean_flux_error Err. Fl. BP Error in the mean flux in the integrated BP band. Errors are computed from the dispersion about the weighted mean of the input calibrated photometry. s**-1 stat.error;phot.flux;em.opt.B
phot_bp_mean_mag Mag BP Mean magnitude in the integrated BP band. This is computed from the BP-band mean flux applying the magnitude zero-point in the Vega scale. No error is provided for this quantity as the error distribution is only symmetric in flux space. For errors small compared to the flux (less than 10%, say), the magnitude error is well approximated by 1.09*flux/flux_err. mag phot.mag;em.opt.B
phot_bp_rp_excess_factor BP/RP excess BP/RP excess factor estimated from the comparison of the sum of integrated BP and RP fluxes with respect to the flux in the G band. This measures the excess of flux in the BP and RP integrated photometry with respect to the G band. This excess is believed to be caused by background and contamination issues affecting the BP and RP data. Therefore a large value of this factor for a given source indicates systematic errors in the BP and RP photometry. N/A stat.fit.goodness
radial_velocity RV Spectroscopic radial velocity in the solar barycentric reference frame. The radial velocity provided is the median value of the radial velocity measurements at all epochs. km/s spect.dopplerVeloc
radial_velocity_error Err. RV The radial velocity error is the error on the median to which a constant noise floor of 0.11 km/s has been added in quadrature to take into account the calibration contribution. km/s stat.error;spect.dopplerVeloc
astrometric_gof_al GoF Goodness-of-fit statistic of the astrometric solution for the source in the along-scan direction (you probably want to use RUWE instead of this). [Note gof] N/A stat.fit.goodness
astrometric_params_solved PS This is a binary code indicating which astrometric parameters were estimated for the source. A set bit means the parameter was estimated. The least-significant bit represents α, the next bits δ, parallax, PM(RA) and PM(De). For Gaia DR2 the only relevant values are 31 (all five parameters solved) and 3 (only positions). N/A meta.code
random_index Random Random index that can be used to deterministically select subsets N/A meta.code
l l Galactic longitude (converted from ra, dec) deg pos.galactic.lon
b b Galactic latitude (converted from ra, dec) deg pos.galactic.lat
phot_g_n_obs #Obs G Number of observations contributing to G photometry N/A meta.number
phot_variable_flag Var? Photometric variability flag [Note var] N/A meta.code;src.var
phot_rp_n_obs #RP Number of observations (CCD transits) that contributed to the integrated RP mean flux and mean flux error. N/A meta.number;obs;phot.mag;em.opt.R
phot_bp_n_obs #BP Number of observations (CCD transits) that contributed to the integrated BP mean flux and mean flux error. N/A meta.number;obs;phot.mag;em.opt.B
bp_rp BP-RP BP-RP color mag phot.color;em.opt.B;em.opt.R
bp_g BP-G BP-G color mag phot.color;em.opt.B;em.opt.V
g_rp G-RP G-RP color mag phot.color;em.opt.V;em.opt.R
phot_rvs_mean_mag RVS Approximate estimate for the RVS magnitude. This uses formulae (2) and (3) from 2018arXiv180409365G. Outside of the range 0.1<BP-G<1.7 we use Eq. 3 (which means the value is probably fairly bogus). mag phot.color;em.opt.I
rv_nb_transits #RV Number of transits (epochs) used to compute radial velocity. N/A stat.number;obs;spect.dopplerVeloc
ref_epoch Epoch Reference epoch to which the astrometic source parameters are referred, expressed as a Julian Year in TCB. yr meta.ref;time.epoch
astrometric_delta_q δq(HIP) Hipparcos/Gaia data discrepancy (Hipparcos subset of TGAS only) [Note dq] N/A stat.value
astrometric_excess_noise Ex. Noise Excess noise of the source [Note en] mas stat.value
astrometric_excess_noise_sig Sig. Noise Significance of excess noise [Note en] N/A stat.value
astrometric_n_obs_ac #Obs AC Total number of observations AC [Note n] N/A meta.number
astrometric_n_obs_al #Obs AL Total number of observations AL [Note n] N/A meta.number
astrometric_n_bad_obs_ac #Bad AC Number of bad observations AC [Note nb] N/A meta.number
astrometric_n_bad_obs_al #Bad AL Number of bad observations AL [Note nb] N/A meta.number
astrometric_n_good_obs_ac #Good AC Number of good observations AC [Note ng] N/A meta.number
astrometric_n_good_obs_al #Good AL Number of good observations AL [Note ng] N/A meta.number
astrometric_chi2_al χ² AL Astrometric goodness-of-fit (χ²) in the AL direction; χ² values were computed for the ‘good’ AL observations of the source, without taking into account the astrometric excess noise (if any) of the source. They do, however, take into account the attitude excess noise (if any) of each observation. N/A stat.fit.chi2
astrometric_primary_flag Primary? Only primary sources (for which this flag is True) contribute to the estimation of attitude, calibration, and global parameters. The estimation of source parameters is not affected by primariness. N/A meta.code
astrometric_pseudo_colour Astr. Col. Colour of the source assumed in the final astrometric processing, given as he effective wavenumber of the photon flux distribution in the astrometric (G) band. The value given in this field was astrometrically determined in a preliminary solution, using the chromatic displacement of image centroids calibrated by means of the effective wavenumbers (ν_eff) of primary sources calculated from BP and RP magnitudes. The field is empty when no such determination was possible, in which case a default value of 1.6 1/µm was assumed. um**-1 N/A
mean_varpi_factor_al Par. Fact. Mean parallax factor in the AL direction, computed from all the good observations of the source processed in the astrometry. The value given in this field is typically in the range [−0.23, +0.32] (1st and 99th percentiles). A value outside this range indicates a distribution of observations that is unfavourable for the determination of the parallax, and the calculated parallax could then be more vulnerable to errors, e.g. from the calibration model, not reflected in the formal uncertainties. N/A stat.fit.param
visiblilty_periods_used #Obs Number of visibility periods used in Astrometric solution. A visibility period is a group of observations separated from other groups by a gap of at least 4 days. N/A stat.number;obs
astrometric_sigma5d_max Max σ The longest principal axis in the 5-dimensional error ellipsoid. This is useful for filtering out cases where one of the five parameters, or some linear combination of several parameters, is particularly ill-determined. It is measured in mas and computed as the square root of the largest singular value of the scaled 5 × 5 covariance matrix of the astrometric parameters. mas stat.error;obs;stat.max
matched_observations Matched_observations The number of observations (detection transits) that have been matched to a given source during the last internal crossmatch revision. N/A meta.number
astrometric_priors_used Prior Type of prior used in in the astrometric solution [Note pri] N/A N/A
astrometric_relegation_factor Releg. fct. Relegation factor of the source calculated as per Eq. (118) in 2012A&A...538A..78L used for the primary selection process. N/A arith.factor
astrometric_weight_ac Weight AC Mean astrometric weight of the source [Note maw] mas**-2 stat.weight;stat.mean
astrometric_weight_al Weight AL Mean astrometric weight of the source [Note maw] mas**-2 stat.weight;stat.mean
duplicated_source Dup? During data processing, this source happened to have been duplicated and one source only has been kept. [Note dup] N/A N/A
ra_dec_corr RA-Dec-Corr Correlation between right ascension and declination [Note cr] N/A stat.correlation
ra_pmra_corr RA-PM(RA) Corr. Correlation between right ascension and proper motion in right ascension [Note cr] N/A stat.correlation
ra_pmdec_corr RA-PM(Dec) Corr. Correlation between right ascension and proper motion in declination [Note cr] N/A stat.correlation
dec_pmra_corr Dec-PM(RA) Corr. Correlation between declination and proper motion in right ascension [Note cr] N/A stat.correlation
dec_pmdec_corr Dec-PM(Dec) Corr. Correlation between declination and proper motion in declination [Note cr] N/A stat.correlation
pmra_pmdec_corr PM(RA)-RM(Dec)-RA Correlation between proper motion in right ascension and proper motion in declination [Note cr] N/A stat.correlation
ra_parallax_corr RA-π Corr. Correlation between right ascension and parallax [Note cr] N/A stat.correlation
dec_parallax_corr RA-Dec Corr. Correlation between declination and parallax [Note cr] N/A stat.correlation
parallax_pmra_corr π-PM(RA) Corr. Correlation between parallax and proper motion in right ascension [Note cr] N/A stat.correlation
parallax_pmdec_corr π-PM(Dec) Corr. Correlation between parallax and proper motion in declination [Note cr] N/A stat.correlation
scan_direction_mean_k1 Scan dir. k1 Mean position angle of scan directions across the source [Note sk] deg N/A
scan_direction_strength_k1 Scan conc. k1 Degree of concentration of scan directions across the source [Note sk] N/A N/A
scan_direction_mean_k2 Scan dir. k2 Mean position angle of scan directions across the source [Note sk] deg N/A
scan_direction_strength_k2 Scan conc. k2 Degree of concentration of scan directions across the source [Note sk] N/A N/A
scan_direction_mean_k3 Scan dir. k3 Mean position angle of scan directions across the source [Note sk] deg N/A
scan_direction_strength_k3 Scan conc. k3 Degree of concentration of scan directions across the source [Note sk] N/A N/A
scan_direction_mean_k4 Scan dir. k4 Mean position angle of scan directions across the source [Note sk] deg N/A
scan_direction_strength_k4 Scan conc. k4 Degree of concentration of scan directions across the source [Note sk] N/A N/A
priam_flags Priam? Flags describing the status of the astrophysical parameters Teff, A G and E[BP-RP] (i.e., those determined by Apsis-Priam). See the release documentation. N/A meta.code
flame_flags FLAME? Flags describing the status of the astrophysical parameters radius and luminosity (i.e., those determined by Apsis-FLAME). See the release documentation. N/A meta.code
teff_val T_eff Effective temperature of the star K phys.temperature
teff_percentile_lower T_eff low Lower uncertainty bound of the effective temperature estimate from Apsis-Priam. This is the 16th percentile of its PDF. K stat.min;phys.temperature
teff_percentile_upper T_eff high Upper uncertainty bound of the effective temperature estimate from Apsis-Priam. This is the 84th percentile of its PDF. K stat.max;phys.temperature
a_g_val A_G Line-of-sight extinction in the G band, A_G mag phys.absorption
a_g_percentile_lower A_G low Lower uncertainty bound of the line-of-sight extinction in the G-band estimate from Apsis-Priam. This is the 16th percentile of its PDF. mag stat.min;phys.absorption
a_g_percentile_upper A_G high Upper uncertainty bound of the line-of-sight extinction in the G-band estimate from Apsis-Priam. This is the 84th percentile of its PDF. mag stat.max;phys.absorption
e_bp_min_rp_val E(BP-RP) Line-of-sight reddening E(BP-RP) mag phys.absorption
e_bp_min_rp_percentile_lower E(BP-RP) low Lower uncertainty bound of the line-of-sight reddening E(BP-RP) estimate from Apsis-Priam. This is the 16th percentile of its PDF. mag stat.min;phys.absorption
e_bp_min_rp_percentile_upper E(BP-RP) high Upper uncertainty bound of the line-of-sight reddening E(BP-RP) estimate from Apsis-Priam. This is the 84th percentile of its PDF. mag stat.max;phys.absorption
radius_val Radius Stellar radius in solar radii solRad phys.size.radius
radius_percentile_lower R low Lower uncertainty bound of the radius estimate from Apsis-FLAME. This is the 16th percentile of its PDF. solRad stat.min;phys.size.radius
radius_percentile_upper R high Upper uncertainty bound of the radius estimate from Apsis-FLAME. This is the 84th percentile of its PDF. solRad stat.max;phys.size.radius
lum_val lum Stellar luminosity in solar luminosities. solLum phys.luminosity
lum_percentile_lower solLum low Lower uncertainty bound of the luminosity estimate from Apsis-FLAME. This is the 16th percentile of its PDF. solLum stat.min;phys.luminosity
lum_percentile_upper solLum high Upper uncertainty bound of the luminosity estimate from Apsis-FLAME. This is the 84th percentile of its PDF. solLum stat.max;phys.luminosity
feh [Fe/H] Fe/H as log10 of solar ratio ('dex') N/A phys.abund.Fe
a0 a_0 Monochromatic extinction at lambda = 547.7nm mag phys.absorption
mass Mass Initial mass of the star solMass phys.mass
age Age Age of the star Gyr time.age
logg log(g) Logarithm of surface gravity. log(cm/(s**2)) phys.gravity
nobs N_obs Nominal number of observations, scaled to the shorter time base of GDR2 (a function with ecliptic latitude). N/A meta.number;obs
index_parsec Phot. Foreign key into the photometry/extinction table. N/A meta.id.cross

Columns that are parts of indices are marked like this.

Other

The following services may use the data contained in this table:

VOResource

VO nerds may sometimes need VOResource XML for this table.

Notes

More Information on the Base Resource

Query Validator

A web form for validating queries against the Gaia DR2 data model without actually submitting them is available at http://gaia.ari.uni-heidelberg.de/adql-validator.html

Associate Photometry

To add simulated multi-band photometry, join gdr2mock.main with gdr2mock.photometry USING (index_parsec).

Nominal error model

The nominal error model for the astrometry is way too optimistic for stars with an apparent G magnitude smaller than 12. For stars in this range a factor of approximately five needs to be added to the reported values. For details refer to https://doi.org/10.1051/0004-6361/201832727. For the photometry the nominal error model seems to be too conservative and the GDR2 photometry seems to be more precise (cf. https://doi.org/10.1051/0004-6361/201832756).

Coping with the table size

gdr2mock.main is a large table. A sequential scan of it will take about an hour on a machine not otherwise loaded. Therefore, only do queries on the whole table with tested queries that actually yield what you expect them to yield. For development, please restrict yourself to reasonably sized-subsets. The recommended pattern is to use common table expressions (CTEs) like this:

WITH sample AS (
        SELECT * FROM gdr2mock.main
        WHERE distance(ra, dec, 66.73, 75.87)<2)
SELECT ra, dec, 1/parallax
FROM sample
WHERE age BETWEEN 0.2 and 0.3

When extending this kind of query to the whole sky (or other regions), all you need to do is edit the statement after WITH.

Note that CTEs are not yet available on all TAP services. On services that do not have them, you can substitute them with subqueries in many situations.

Preferred form of spatial constraints

Please note that the standard (and fast) pattern for spatial selections is cone-like; you can equivalently write:

WHERE distance(ra, dec, 66.73, 75.87)<2

or:

WHERE 1=CONTAINS(
        POINT(ra, dec),
        CIRCLE(66.73, 75.87, 2))

Both assume ADQL 2.1; in ADQL 2.0 (which still runs on the major Gaia services in early 2018; this form also runs on ADQL 2.1), it's:

WHERE 1=CONTAINS(
        POINT('', ra, dec),
        CIRCLE('', 66.73, 75.87, 2))

Do not use “rectangular” selection ("ra between 1 and 2" or "ra<=2 and ra>=1") – this will be slow and may introduce all kinds of weird effects depending on the coordinate frame.

Chunking the catalog

In some situations, you want to exhaustively partition the catalog. To do that, use the source_id column, which has a healpix number in the upper 29 bits. The following partition will yield chunks of no more than 10 million objects and was used to produce the dump of the catalog:

limits = [0, 164291313590476000L, 191269676664280480L,
        207871746248160160L, 248685167308531040L, 271513012986365600L,
        345530573214495872L, 411247098650686208L, 431307932731207552L,
        458978048704340352L, 474888365388891648L, 510167763254636032L,
        523311068341874048L, 539913137925753728L, 674113200395447936L,
        936287549240881152L, 1060803071119978752L, 1276629975710414592L,
        1473087799119657216L, 1738720912461732096L, 1791985885710012928L,
        1810663213991877376L, 1820347754582473984L, 1823806519079115520L,
        1826573530676428800L, 1832107553871055360L, 1843175600260308480L,
        1860469422743516416L, 1869462210434784768L, 1920651924985080320L,
        1961465346045451264L, 1974608651132689408L, 1990518967817240832L,
        2005737531602463744L, 2017497330891045120L, 2020956095387686656L,
        2023723106985000192L, 2025798365682984960L, 2029948883078955008L,
        2032024141776939776L, 2034099400474924800L, 2038249917870894848L,
        2045859199763506432L, 2050701470058804480L, 2059002504850744320L,
        2064536528045370880L, 2072145809937982464L, 2075604574434624000L,
        2083905609226563840L, 2103966443307085056L, 2152389146260067584L,
        2168991215843947264L, 2177984003535215360L, 2185593285427826944L,
        2199428343414393344L, 2207729378206333184L, 2222256189092227840L,
        2270678892045210368L, 2544613040179225088L, 2768049226662272512L,
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Please don't use it to dump the catalog yourself; it might still come in handy if you filter out a large part of the objects but still have more than 10 million matches. Just regularly remove separation points.

The way to use it in python would be to say:

# make svc a TapService object with the right access URL, and then:
for low, high in zip(limits[:-1], limits[1:]):
        partial = svc.run_sync("WITH sample AS ("
                " SELECT * FROM gdr2mock.main"
                " WHERE source_id BETWEEN {} AND {})"
                " <your query against sample here>".format(low, high-1))

RVS magnitudes

In particular to aid in designing studies involving radial velocities, we give a column phot_rvs_mean_mag based on the approximations given in 2018arXiv180409365G. This is going to be severely off outside of 0.1&lt;BP-G&lt;1.7 (which only affects less than 50 ppm of the objects).

Dump of the catalog

While we strongly recommend that you try to structure your problem so as to make it work through ADQL, for the benefit of those who want to mirror the table or use it in services of their own, we provide a dump of the data in the form of FITS binary tables files; warning: pulling it transfers about 300 gigabytes, and dealing with this data volume is nontrivial.

Having said that: The dump files are available from http://dc.zah.uni-heidelberg.de/gdr2mock/q/download.