Table information for 'gedr3mock.main'

General

Table Description: A synthetic Milky Way catalog mimicking Gaia EDR3 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 EDR3 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 scaled GDR2 errors. Additional magnitude limit per HEALpix maps are provided, based on the mode in the magnitude distribution of Gaia DR2 data.

For a list of all services and tables belonging to this table's resource, see Information on resource 'Gaia early DR3 Mock Catalogue (gedr3mock)'

Resource Reference URL: http://dc.zah.uni-heidelberg.de/tableinfo/gedr3mock.main

Citing this table

@MISC{vo:gedr3mock_main,
  year=2019,
  title={Gaia early DR3 Mock Catalogue (gedr3mock)},
  author={Rybizki, J. and Demleitner, M. and Bailer-Jones, C. and Tio, P. D. and Cantat-Gaudin, T. and Fouesneau, M. and Chen, Y. and Andrae, R. and Girardi, L. and Sharma, S.},
  url={http://dc.zah.uni-heidelberg.de/tableinfo/gedr3mock.main},
  howpublished={{VO} resource provided by the {GAVO} Data Center},
  doi = {10.21938/dBrplG8I1KR7RyfkMXTyYA}
}

Copyright, License, Acknowledgements

• To the extent possible under law, the publisher has waived all copyright and related or neighboring rights to the Gaia eDR3 mock catalogue. For details, see the Creative Commons CC0 1.0 Public Domain dedication. Of course, you should still give proper credit when using this data as required by good scientific practice.

  
• https://spdx.org/licenses/CC0-1.0.html

Table Documentation

WITH sample AS (
        SELECT * FROM gedr3mock.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

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

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

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

limits = [
        0,  161000525582565376,  190241590646669312,
        205827580287254528,  238741701382897664,  260424586078715904,
        281214461174349824,  376312424451538944,  419212653748027392,
        434001497458475008,  459290814653136896,  473006912971997184,
        506257553345216512,  518036827572011008,  530296897617788928,
        561471797476720640,  759627535381168128,  944006495668797440,
        1040369721951649792, 1255394304688390144, 1474976775439122432,
        1741324705534574592, 1790290699963334656, 1808727482736574464,
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        1857229242740244480, 1864013813599174656, 1871967199678169088,
        1929099026448252928, 1960210463870418944, 1971591440010051584,
        1980320050506104832, 1997472672417579008, 2006800825988415488,
        2016674749643489280, 2020634984368308224, 2023410014277861376,
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        4067952504602624000, 4068619805081468928, 4069903038230298624,
        4071285845900918784, 4073456488012578816, 4076157988082024448,
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        4084865329900027904, 4088450768598728704, 4089692289025179648,
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        4093254363101790208, 4094818177874132992, 4096497029050531840,
        4097917357555449856, 4099795632653336576, 4101931537069506560,
        4103357569290993664, 4104180175787261952, 4105068890420150272,
        4106540724172881920, 4107362780913336320, 4108191091126173696,
        4109639525897076736, 4110886028485591040, 4112922083502063616,
        4116166123840339968, 4117045630063345664, 4118075357062496256,
        4119249051365408768, 4120552178802753536, 4123114934248669184,
        4125097937829101568, 4132304137037545472, 4138499163505557504,
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        4203246001651187712, 4204504083471532032, 4207748673565622272,
        4214137351519076352, 4236123701424160768, 4249140888304877568,
        4252016626607587328, 4252794771602407424, 4253810170590658560,
        4254961050027294720, 4256649796808343552, 4259413384925020160,
        4261775445139128320, 4263664062518263808, 4265313123801497600,
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        4280098668977061888, 4285079628449579008, 4288820063928057856,
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        4508939336871313408, 4513839894556049408, 4517168391131234304,
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        4685905458483953664, 4752009334496428032, 5048627575618797568,
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        5305849353045278720, 5309762411849318400, 5313139321295863808,
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        5337642556396142592, 5340266987572428800, 5343656403963740160,
        5350462621457842176, 5354722576180445184, 5359458413279444992,
        5372887470343979008, 5404963145103966208, 5410571788276989952,
        5424220988185247744, 5442675225705578496, 5508257452170149888,
        5521906755157622784, 5529933739796201472, 5540320791784062976,
        5546108208675815424, 5583112135069663232, 5595949689238192128,
        5601613136193912832, 5614335276001263616, 5621941697442217984,
        5640128856716214272, 5671690544149954560, 5700966140751118336,
        5716945240158371840, 5766837504414056448, 5792882426615169024,
        5802883584381419520, 5818014616726274048, 5824544513204420608,
        5827317722047840256, 5831197726783569920, 5833472822499868672,
        5834633528821678080, 5835820554703077376, 5836584268607782912,
        5844699317255798784, 5848652851831635968, 5850924511574097920,
        5852524266632773632, 5854022419945095168, 5857492066325495808,
        5858927272597127168, 5860855678553292800, 5862852907065409536,
        5865046604561514496, 5869000963771072512, 5871370892365266944,
        5874081463405641728, 5875585801470869504, 5876931225746145280,
        5879421001107767296, 5881995988620541952, 5884080800105758720,
        5887131841793622016, 5888770698234560512, 5890363031589748736,
        5893058312546549760, 5895628043019354112, 5900250699140169728,
        5904354901168750592, 5913874541561511936, 5919725420890030080,
        5924441569858945024, 5926972130230009856, 5929102227850395648,
        5930678309049335808, 5931684087310843904, 5932349738522247168,
        5933174578401509376, 5934095969145585664, 5935796020280557568,
        5937111070547116032, 5938525764054941696, 5940292851039469568,
        5942406318546485248, 5944115818609508352, 5947882333129408512,
        5950513670613106688, 5952165721193578496, 5953570931413614592,
        5956003807048499200, 5957924550783008768, 5959238638976892928,
        5960370448758734848, 5961583038285479936, 5963056933622513664,
        5965315330505965568, 5968019063958405120, 5969643214431322112,
        5971116078976204800, 5972674464909885440, 5976388340310605824,
        5978043002231193600, 5979719860542767104, 5980600672435830784,
        5983573786237075456, 5986047549960617984, 5988772071054770176,
        5991739515499184128, 5995044510013325312, 6000602713090424832,
        6009501438651138048, 6018305022096310272, 6022181968815325184,
        6026558643569164288, 6028494643027509248, 6029656311421992960,
        6032022529164443648, 6035830893845676032, 6046287902260854784,
        6054119551786811392, 6057337650522619904, 6060242697682157568,
        6064027697741299712, 6070987949942505472, 6076648098363342848,
        6090704048854401024, 6105062502482051072, 6131616773444730880,
        6189459537561387008, 6225770977788166144, 6260198885877088256,
        6362808609516027904, 6442940742070435840, 6618300339755941888,
        6652038613536079872, 6679890273859796992, 6706241753646628864,
        6716109767426703360, 6722991267107569664, 6726135492405886976,
        6728692372336541696, 6733950168221089792, 6736718979017998336,
        6755124150831939584, 6762744900283793408, 6780826540801261568,
        6866936202679812096, 8931576916874782720]

# 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 gedr3mock.main"
                " WHERE source_id BETWEEN {} AND {})"
                " <your query against sample here>".format(low, high-1))

zcat dump.gz | split -a4 -l 5000 -d - part

Columns

Sorted by DB column index. [Sort alphabetically]

Columns that are parts of indices are marked like this.

VOResource

VO nerds may sometimes need VOResource XML for this table.

Notes

• Note gof

  This is the “gaussianized chi-square”, which for good fits should approximately follow a normal distribution with zero mean value and unit standard deviation. Values exceeding, say, +3 thus indicate a bad fit to the data.

  This statistic is computed according to the formula

  gof = sqrt(9ν/2) [(χ²/ν)^(1/3) + 2/(9ν) − 1],

  where χ² (astrometric_chi2_al) is the AL chi-square statistic and ν = (astrometric_n_good_obs_al − 5) is the number of degrees of freedom for a source update. Here, 5 is the number of astrometric parameters. Note that only “good” (i.e. not strongly downweighted) observations are included in χ² and ν.

  An alternative indicator of bad fits is the astrometric excess noise. In AGIS the source update deals with bad fits by adding astrometric excess noise to the formal observation noise. This reduces the weight of the observations and inflates the covariance of the estimated astrometric parameters correspondingly. However, the chi-square values used to calculate astrometric_gof_al do not take into account the astrometric excess noise, and astrometric_gof_al can therefore always be used as a goodness-of-fit indicator of the source solution in AGIS.

• Note id

  For the contents of Gaia DR2, the source ID consists of a 64-bit integer, least significant bit = 1 and most significant bit = 64, comprising:

  • a HEALPix index number (sky pixel) in bits 36 - 63; by definition the

    smallest HEALPix index number is zero.

  • a 2-bit Data Processing Centre code in bits 33 - 35; for example

    MOD(source_id / 4294967296, 8) can be used to distinguish between sources initialised via the Initial Gaia Source List by the Torino DPC (code = 0) and sources otherwise detected and assigned by Gaia observations (code > 0)

  • a 25-bit plus 7 bit sequence number within the HEALPix pixel in bits 1

    to 32 split into:

    • a 25 bit running number in bits 8 - 32; the running numbers are

      defined to be positive, i.e. never zero (except in the case of forced empty windows)

    • a 7-bit component number in bits 1 - 7

  This means that the HEALpix index level 12 of a given source is contained in the most significant bits. HEALpix index of 12 and lower levels can thus be retrieved as follows:

  • HEALpix level 12 = source_id / 34359738368
  • HEALpix level 11 = source_id / 137438953472
  • HEALpix level 10 = source_id / 549755813888
  • HEALpix level n = source_id / 2^35 * 4^(12 - level).
• Note phot

  Note that several systematics have been found for both Gaia G and BP photometry on the level of 10s of mmags. See 2018A&A...616A..17A and 2018A&A...616A...4E for a description of the problem as well as some proposed empirical corrections. For sources brighter than 16.5, see also 2018MNRAS.479L.102C and 2018A&A...617A.138W, where the latter also discusses systematics in BP.

• Note id

  For the contents of Gaia DR1 (and hence TGAS), which does not include Solar System objects, the source ID consists of a 64-bit integer, least significant bit = 1 and most significant bit = 64, comprising:

  • a HEALPix index number (sky pixel) in bits 36 - 63; by definition the

    smallest HEALPix index number is zero.

  • a 2-bit Data Processing Centre code in bits 34 - 35; for example

    MOD(source_id / 4294967296, 8) can be used to distinguish between sources initialised via the Initial Gaia Source List by the Torino DPC (code = 0) and sources otherwise detected and assigned by Gaia observations (code > 0)

  • a 25-bit plus 7 bit sequence number within the HEALPix pixel in bits 1

    to 32 split into:

    • a 25 bit running number in bits 8 x 32; the running numbers are

      defined to be positive, i.e. never zero (except in the case of forced empty windows)

    • a 7-bit component number in bits 1 x 7 one spare bit in bit 33

  This means that the HEALpix index level 12 of a given source is contained in the most significant bits. HEALpix index of 12 and lower levels can thus be retrieved as follows:

  • HEALpix level 12 = source_id / 34359738368
  • HEALpix level 11 = source_id / 137438953472
  • HEALpix level 10 = source_id / 549755813888
  • HEALpix level n = source_id / 2^35 * 4^(12 - level).
• Note dq

  In the TGAS solution astrometric_delta_q (ΔQ) indicates the discrepancy between the Hipparcos proper motion and the TGAS proper motion. A large value of ΔQ could indicate non-linear motion (e.g. in a binary).

  For more details, see the paper referenced in the source metadata.

• Note en

  This is the excess noise ϵ_i of the source. It measures the disagreement, expressed as an angle, between the observations of a source and the best-fitting standard astrometric model (using five astrometric parameters). The assumed observational noise in each observation is quadratically increased by ϵ_i. in order to statistically match the residuals in the astrometric solution. A value of 0 signifies that the source is astrometrically well-behaved, i.e. that the residuals of the fit statistically agree with the assumed observational noise. A positive value signifies that the residuals are statistically larger than expected.

  The significance of ϵ_i is given by astrometric_excess_noise_sig. If this is larger than 2, then ϵ_i is probably not significant, and the source may be astrometrically well-behaved even if ϵ_i is large.

  The excess noise may absorb all kinds of modelling errors that are not accounted for by the observational noise (image centroiding error) or the excess attitude noise. Such modelling errors include LSF and PSF calibration errors, geometric instrument calibration errors, and part of the high-frequency attitude noise. These modelling errors are particularly important in the early data releases, but should decrease as the astrometric modelling of the instrument and attitude improves over the years.

  Additionally, sources that deviate from the standard five-parameter astrometric model (e.g. unresolved binaries, exoplanet systems, etc.) may have positive excess noise. . Given the many other possible contributions to the excess noise, the user must study the empirical distributions of ϵ_i and astrometric_excess_noise_sig. to make sensible cutoffs before filtering out sources for their particular application.

  In Gaia DR1, the excess source noise has the same interpretation as described above for both the primary (TGAS) and secondary data sets. It measures the disagreement between the five-parameter model and the observations, augmented by the different priors used. Thus, in TGAS the excess noise may be increased if the proper motion seen during the 14 months of Gaia observations are not in agreement with the proper motion inferred from the Tycho-2/Gaia comparison. In the secondary solution the excess noise may be increased if the Gaia observations indicate a proper motion or parallax several times larger than the prior uncertainty.

• Note nb

  Number of AC or AL observations (= CCD transits) that were strongly downweighted in the astrometric solution of the source, and therefore contributed little to the determination of the astrometric parameters. An observation is considered to be strongly downweighted if its downweighting factor w < 0.2, which means that the absolute value of the astrometric residual exceeds 4.83 times the total uncertainty of the observation, calculated as the quadratic sum of the centroiding uncertainty, excess source noise, and excess attitude noise.

• Note ng

  Number of AC/AL observations (= CCD transits) that were not strongly downweighted in the astrometric solution of the source. Strongly downweighted observations (with downweighting factor w<0.2, are instead counted in astrometric_n_bad_obs_ac/_al. The sum of astrometric_n_good_obs_ac/_al and astrometric_n_bad_obs_ac/_al equals astrometric_n_obs_ac/_al, the total number of AC/AL observations used in the astrometric solution of the source.

• Note n

  Total number of AC/AL observations (= CCD transits) used in the astrometric solution of the source, independent of their weight. Note that some observations may be strongly downweighted (see astrometric_n_bad_obs_ac/_al).

  Nearly all sources having G<13 will have AC observations from 2d windows, while fainter than that limit only ∼ 1% of stars (the so–called “calibration faint stars”) are assigned 2d windows resulting in AC observations (this does not apply to AL).

• Note pri

  Type of prior used in the astrometric solution:

  0:

  No prior used

  1:

  Galaxy Bayesian Prior for parallax and proper motion

  2:

  Galaxy Bayesian Prior for parallax and proper motion relaxed by factor 10

  3:

  Hipparcos prior for position

  4:

  Hipparcos prior for position and proper motion

  5:

  Tycho2 prior for position

  6:

  Quasar prior for proper motion

  The Galaxy Bayesian Prior is defined in 2015A&A...583A..68M, where it is denoted σ_ϖ,F90 (for the parallax) and σ_μ,F90 (for proper motion).

  For Gaia DR1 the only types of priors used are 2 (for the secondary data set), 3 (for the Hipparcos subset of the primary data set), or 5 (for the non-Hipparcos subset of the primary data set). Type 6 was used for internal calibration purposes and alignment of the reference frame, but the corresponding astrometric results are in general not published.

• Note maw

  Mean astrometric weight of the source in AC or AL direction. The mean astrometric weight of the source is calculated as per Eq. (119) in 2012A&A...538A..78L.

• Note cr

  All off-diagonal members entries of the covariance matrix (ra_dec_corr, ra_pmra_corr, ra_pmdec_corr, dec_pmra_corr, dec_pmdec_corr, pmra_pmdec_corr, ra_parallax_corr, dec_parallax_corr, parallax_pmra_corr, parallax_pmdec_corr) are dimensionless values normalised to between -1 and 1.

• Note dup

  This may indicate observational, cross-matching or processing problems, or stellar multiplicity, and probable astrometric or photometric problems in all cases. In DR1, for close doubles with separations below some 2 arcsec, truncated windows have not been processed, neither in astrometry and photometry. The transmitted window is centred on the brighter part of the acquired window, so the brighter component has a better chance to be selected, even when processing the fainter transit. If more than two images are contained in a window, the result of the image parameter determination is un-predictable in the sense that it might refer to either (or neither) image, and no consistency is assured.

• Note var

  Flag indicating if variability was identified in the photometric G band:

  • NULL: source not processed and/or exported to catalogue
  • "CONSTANT": Source not identified as variable (not in this release)

  • "VARIABLE": source identified and processed as variable;

    see ivo://esavo/gaia/tap for more information on these objects.

  Note that for this data release only a small subset of (variable) sources was processed and/or exported, so for many (known) variable sources this flag is set to NULL.

• Note sk

  The scan_direction_strength and scan_direction_mean quantify the distribution of AL scan directions across the source. scan_direction_mean_kK (K=1..4) is 1/K times the argument of the trigonometric moments m_k = exp(ikθ), where θ is the position angle of the scan and the mean value is taken over the nObs[0] AL observations contributing to the astrometric parameters of the source.

  θ is defined in the usual astronomical sense: θ = 0 when the FoV is moving towards local North, and θ = 90° towards local East.

  scan_direction_mean_kK is an angle between -180°/K and 180°/K, giving the mean position angle of the scans at order K. The different orders K are statistics of the scan directions modulo 360°/K.

  For example, at first order (K=1), θ = 10° and θ = 190° count as different directions, but at second order (K = 2) they are the same. Thus, scan_direction_mean_k1 is the mean direction when the sense of direction is taken into account, while scan_direction_mean_k2 is the mean direction without regard to the sense of the direction.

  For example, scan_direction_mean_k1 = 0 means that the scans preferentially go towards North, while scan_direction_mean_k2 = 0 means that they preferentially go in the North-South direction, and scan_direction_mean_k4 = 0 that they preferentially go either in the North-South or in the East-West direction.

  The scan_direction_strength is a number between 0 and 1, where 0 means that the scan directions are well spread out in different directions, while 1 mean sthat they are concentrated in a single direction (given by scan_angle_mean).