A Gaia DR2 mock stellar catalog
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.
2018-03-13T13:08:00
1000
10.21938/SYx.JVejuu66uzDiLcfRlA
\internallink{/tableinfo/gdr2mock.main}
astrostatistics-techniques
milky-way-galaxy
astronomical-simulations
Rybizki, J.; Demleitner, M.; Fouesneau, M.;
Bailer-Jones, C.; Rix, H.-W.; Andrae, R.
Added a column phot_rvs_mean_mag based on the approximations
given in :bibcode:`2018A&A...616A...1G`. This is going to be severely off
outside of 0.1<BP-G<1.7; the column is also not included
in the dumps.
Two of the dump files were truncated due to overflows; also, some
stars beyond DR1's top source_id were missing in the dump. If
you have downloaded the dump before today, please re-download
dump-059114520-059124586.fits, dump-058933326-058943392.fits, and
dump-100507267-100663295.fits.
The catalog was revised and several spurious previsously unextincted
OB and TP-AGB stars were removed (see the arXiv manuscript for details).
In consequence, the number of sources has decreased by about 0.5%.
The uncertainties in BP flux were truncated for uncertainties higher
than 100 mag in BP (they are replaced by NULL. These
high values come from the nominal error model for single-transits
and are probably too pessimistic. In short: Do not use bp fluxes
if the errors are NULL.
Also, made available a dump of the (non-null) rows of gdr2mock.main.
2018PASP..130g4101R
Research
Simulation
Optical
Infrared
An internal stream to build the three-parameter sets for the
priam/FLAME-determined stuff.
bandName, bandLabel, descLabel, ucd
BP, bp, Gaia BP, opt.B
RP, rp, Gaia RP, opt.R
U, ju, Johnson U, opt.U
B, jb, Johnson B, opt.B
V, jv, Johnson V, opt.V
R, jr, Johnson R, opt.R
I, ji, Johnson I, opt.I
J, jj, Johnson J, ir.J
H, jh, Johnson H, ir.H
K, jk, Johnson K, ir.K
u, su, SDSS u, opt.U
g, sg, SDSS g, opt.V
r, sr, SDSS r, opt.R
i, si, SDSS i, opt.I
z, sz, SDSS z, opt.I
Extinctions for Gaia DR2 mock photometry. This is joined into
the parsec_phot view.
a_bp: A_BP,
a_rp: A_RP,
a_ju: A_U,
a_jb: A_B,
a_jv: A_V,
a_jr: A_R,
a_ji: A_I,
a_jj: A_J,
a_jh: A_H,
a_jk: A_K,
a_su: A_u,
a_sg: A_g,
a_sr: A_r,
a_si: A_i,
a_sz: A_z,
index_parsec: index_parsec
bandName, bandLabel, descLabel, ucd
G, gaia_g, Gaia G, opt.V
U, ubv_u, Johnson U, opt.U
B, ubv_b, Johnson B, opt.B
V, ubv_v, Johnson V, opt.V
R, ubv_r, Johnson R, opt.R
I, ubv_i, Johnson I, opt.I
J, ubv_j, Johnson J, ir.J
H, ubv_h, Johnson H, ir.H
K, ubv_k, Johnson K, ir.K
u, sdss_u, SDSS u, opt.U
g, sdss_g, SDSS g, opt.V
r, sdss_r, SDSS r, opt.R
i, sdss_i, SDSS i, opt.I
z, sdss_z, SDSS z, opt.I
J, 2mass_j, 2MASS J, ir.J
H, 2mass_h, 2MASS H, ir.H
K', 2mass_ks, 2MASS K', ir.K
W1, wise_w1, WISE W1, ir.3-4um
W2, wise_w2, WISE W2, ir.4-8um
W3, wise_w3, WISE W3, ir.8-15um
W4, wise_w4, WISE W4, ir.15-30um
Absolute magnitude for Gaia DR2 mock in various photometric bands.
This is joined with the raw_extinctions to give the gdr2mock.photometry.
mag_gaia_g: gaia_g,
mag_ubv_u: ubv_u,
mag_ubv_b: ubv_b,
mag_ubv_v: ubv_v,
mag_ubv_r: ubv_r,
mag_ubv_i: ubv_i,
mag_ubv_j: ubv_j,
mag_ubv_h: ubv_h,
mag_ubv_k: ubv_g,
mag_sdss_u: sdss_u,
mag_sdss_g: sdss_g,
mag_sdss_r: sdss_r,
mag_sdss_i: sdss_i,
mag_sdss_z: sdss_z,
mag_2mass_j: 2mass_j,
mag_2mass_h: 2mass_h,
mag_2mass_ks: 2mass_ks,
mag_wise_w1: wise_w1,
mag_wise_w2: wise_w2,
mag_wise_w3: wise_w3,
mag_wise_w4: wise_w4,
index_noext: index_parsec
Photometry for the Gaia DR2 mock catalog
This table contains simulated
absolute magnitudes to about 0.1 mag precision for all the stars,
as well as extinctions in several bands; To use it, join it USING (parsec_index) with the
\schema.main table.
CREATE VIEW \curtable AS
(SELECT \colNames FROM
\schema.raw_exts JOIN \schema.raw_phot USING (index_noext))
This contains the data actually generated.
Users probably want to use the gdr2mock.main table that
(fairly well) follows the Gaia DR2 data model.
name, type, unit, description, tablehead
teff, real, K, effective Temperature, Teff
feh, real, , log of metallicity in solar units ('dex'), [Fe/H]
a0, real, mag, monochramatic Extinction at lambda=547.7nm, A_0
lum, real, solLum, Stellar luminosity, Luminosity
glon, double precision, deg, Galactic longitude, l
glat, double precision, deg, Galactic latitude, b
mass, real, solMass, initial mass in solar masses, Mass
age, real, Gyr, Age of the star, Age
logg, real, log(cm/(s**2)), Surface gravity of star, log(g)
gaia_g, real, mag, apparent G mag including extinction, G
gaia_g_bp, real, mag, apparent BP mag including extinction, BP
gaia_g_rp, real, mag, apparent RP mag including extinction, RP
sig_g, real, mag, error in G, error_G
sig_bp, real, mag, error in BP, error_BP
sig_rp, real, mag, error in RP, error_RP
A_G, real, mag, Extinction in G, A_G
parallax, real, mas, parallax in mas, parallax
radial_velocity, real, km/s, Radial velocity in km/s, V_rad
pm_ra, real, mas/yr, Proper motion in projection of right ascension, pm_alpha
pm_dec, real, mas/yr, Proper motion in declination, pm_delta
ebp_rp, real, mag, reddening in BP-RP, E(BP-RP)
parallax_error, real, mas, parallax uncertainty in mas, error_parallax
glon, glat
q3c_ang2ipix(glon, glat)
ebp_rp: EBP-RP
A synthetic Milky Way catalog mimicking
GDR2 in stellar content and data model.
`_ 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 \schema.main"
" WHERE source_id BETWEEN {} AND {})"
" ".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 :bibcode:`2018A&A...616A...1G`. This is going to be severely off
outside of 0.1
band, ucd
rp, R
bp, B
name, label, bands
bp_rp, BP-RP, em.opt.B;em.opt.R
bp_g, BP-G, em.opt.B;em.opt.V
g_rp, G-RP, em.opt.V;em.opt.R
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).
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.
This is the excess noise ϵ\ :sub:`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\ :sub:`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\ :sub:`i` is given by astrometric_excess_noise_sig.
If this is larger than 2, then ϵ\ :sub:`i` is probably not significant,
and the source may be astrometrically well-behaved even if ϵ\ :sub:`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\ :sub:`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.
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.
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.
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).
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 :bibcode:`2015A&A...583A..68M`,
where it is denoted σ\ :sub:`ϖ,F90` (for the parallax) and
σ\ :sub:`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.
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 :bibcode:`2012A&A...538A..78L`\ .
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.
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.
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.
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\ :sub:`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).
-
|sin(beta)| alpha* delta pi mu-alpha* mu-delta
0.025 1.026 0.756 1.180 0.725 0.542
0.075 1.021 0.757 1.180 0.722 0.542
0.125 1.002 0.751 1.169 0.710 0.537
0.175 0.993 0.752 1.167 0.703 0.539
0.225 0.973 0.751 1.158 0.689 0.538
0.275 0.952 0.742 1.143 0.673 0.533
0.325 0.934 0.740 1.136 0.662 0.533
0.375 0.901 0.730 1.146 0.640 0.525
0.425 0.861 0.718 1.098 0.614 0.515
0.475 0.819 0.705 1.072 0.584 0.506
0.525 0.765 0.691 1.043 0.548 0.493
0.575 0.701 0.673 1.009 0.500 0.477
0.625 0.631 0.650 0.970 0.451 0.461
0.675 0.535 0.621 0.922 0.381 0.437
0.725 0.469 0.607 0.850 0.327 0.423
0.775 0.554 0.636 0.808 0.386 0.443
0.825 0.603 0.654 0.779 0.422 0.456
0.875 0.641 0.669 0.755 0.447 0.467
0.925 0.668 0.680 0.731 0.466 0.473
0.975 0.688 0.706 0.713 0.481 0.490
names = ("lat_index sinbetbin alpha_ratio delta_ratio"
" parallax_ratio pmra_ratio pmdec_ratio").split()
for ind, items in enumerate(self.sourceToken.split("\n")[2:]):
yield dict(zip(names,
[ind]+[float(item) for item in items.split()]))
-
|sin(beta)| beta_min [deg] beta_max [deg] N_obs alpha* delta pi mu-alpha* mu-delta
0.025 0.0 2.9 61 0.920 0.666 1.045 0.651 0.478
0.075 2.9 5.7 61 0.916 0.667 1.044 0.648 0.478
0.125 5.7 8.6 62 0.907 0.667 1.043 0.642 0.478
0.175 8.6 11.5 62 0.898 0.668 1.041 0.636 0.479
0.225 11.5 14.5 63 0.887 0.672 1.042 0.628 0.482
0.275 14.5 17.5 65 0.881 0.675 1.044 0.624 0.485
0.325 17.5 20.5 66 0.871 0.678 1.046 0.618 0.488
0.375 20.5 23.6 68 0.853 0.679 1.046 0.607 0.489
0.425 23.6 26.7 71 0.833 0.682 1.048 0.594 0.490
0.475 26.7 30.0 75 0.814 0.689 1.053 0.581 0.495
0.525 30.0 33.4 80 0.786 0.697 1.058 0.563 0.498
0.575 33.4 36.9 87 0.751 0.708 1.067 0.536 0.502
0.625 36.9 40.5 98 0.717 0.725 1.088 0.513 0.515
0.675 40.5 44.4 122 0.679 0.774 1.154 0.484 0.545
0.725 44.4 48.6 144 0.646 0.822 1.156 0.451 0.573
0.775 48.6 53.1 106 0.656 0.738 0.943 0.457 0.515
0.825 53.1 58.2 93 0.668 0.711 0.852 0.467 0.496
0.875 58.2 64.2 85 0.679 0.695 0.789 0.473 0.486
0.925 64.2 71.8 80 0.686 0.686 0.741 0.479 0.478
0.975 71.8 90.0 75 0.684 0.690 0.700 0.479 0.479
names = ("lat_index sinbetabs beta_min beta_max n_obs alpha_ratio delta_ratio"
" parallax_ratio pmra_ratio pmdec_ratio").split()
for ind, items in enumerate(self.sourceToken.split("\n")[2:]):
if items.strip():
yield dict(zip(names,
[ind]+[float(item) for item in items.split()]))
Gaia DR2 Mock Bulk Download
A facility to download the raw FITS tables
containing the generated data for the Gaia DR2 mock tables. Almost
all users will want to use the ingested and processed table
gdr2mock.main on the TAP service at http://dc.g-vo.org/tap instead.
Rough metadata for the FITS binary tables is available from
\RSTtable{gdr2mock.generated_data}.
To download the files, visit \RSTservicelink{\rdId/download/static}.
data
/tap/sync
row = self.getFirstVOTableRow()
self.assertEqual(row["source_id"], 1473352350088298497)
self.assertAlmostEqual(row["ra_error"], 0.0390325976461172)
self.assertEqual(row["nobs"], 32)