Table information for 'gdr2ap.main'

General

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

Resource Description: We estimated the stellar astrophysical parameters of 120 million stars over the entire sky that have Gaia parallax and photometry from Gaia DR2, 2MASS, and AllWISE. We provide estimates of log age, log mass, log temperature, log luminosity, log surface gravity, distance modulus, dust extinction (A0), and average grain size (R0) along the lines of sight. In contrast with other catalogs, we do not use a Galactic model as prior but weakly informative ones. Our estimate and uncertainties are quantiles, so they are invariant under monotonic transformations (e.g., log, exp). This means that one can use our median estimate to obtain the median distance or temperature, for instance, and likewise for the uncertainties.

For a list of all services and tables belonging to this table's resource, see Information on resource 'Astrophysical parameters from Gaia DR2, 2MASS and AllWise'

Citing this table

This table has an associated publication. If you use data from it, it may be appropriate to reference 2022arXiv220103252F (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:gdr2ap_main,
  year=2021,
  title={Astrophysical parameters from Gaia DR2, {2MASS} and {AllWise}},
  author={Fouesneau, M. and Andrae, R. and Dharmawardena, T. and Rybizki, J. and Bailer-Jones, C. A. L. and Demleitner, M.},
  url={http://dc.zah.uni-heidelberg.de/tableinfo/gdr2ap.main},
  howpublished={{VO} resource provided by the {GAVO} Data Center}
}

Resource Documentation

The full dataset is available in VAEX-style HDF5, suited in particular for visualisation-intensive all-sky analyses. This dump is about 100 gigabytes in size: http://dc.zah.uni-heidelberg.de/gdr2ap/q/download.

Columns

Sorted by DB column index. [Sort alphabetically]

NameTable Head DescriptionUnitUCD
source_id Source Id Unique source identifier. Note that this *cannot* be matched against the DR1 source_id. [Note id] N/A meta.id;meta.main
a0_best A₀ best multivariate maximum posterior estimate for the dust exctinction A₀ towards this source. mag phys.absorption
a0_p50 A₀ med median of the distribution of dust exctinction A₀ towards this source. mag phys.absorption;stat.median
a0_dist P(A₀) Distribution (min, p16, p25, p50, p75, p84, max) of the dust exctinction A₀ towards this source. In ADQL, write a0_dist[1] for the minimum, a0_dist[2] for the 16th percentile, and so on. mag stat;phys.absorption
r0_best R₀ best multivariate maximum posterior estimate for the average dust grain size extinction parameter. N/A phys.absorption
r0_p50 R₀ med median of the distribution of average dust grain size extinction parameter. N/A phys.absorption;stat.median
r0_dist P(R₀) Distribution (min, p16, p25, p50, p75, p84, max) of the average dust grain size extinction parameter. In ADQL, write r0_dist[1] for the minimum, r0_dist[2] for the 16th percentile, and so on. N/A stat;phys.absorption
loga_best log(age) best multivariate maximum posterior estimate for the log10 of the age. log(yr) time.age
loga_p50 log(age) med median of the distribution of log10 of the age. log(yr) time.age;stat.median
loga_dist P(log(age)) Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the age. In ADQL, write loga_dist[1] for the minimum, loga_dist[2] for the 16th percentile, and so on. log(yr) stat;time.age
logl_best log(L) best multivariate maximum posterior estimate for the log10 of the luminosity. log(solLum) phys.luminosity
logl_p50 log(L) med median of the distribution of log10 of the luminosity. log(solLum) phys.luminosity;stat.median
logl_dist P(log(L)) Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the luminosity. In ADQL, write logl_dist[1] for the minimum, logl_dist[2] for the 16th percentile, and so on. log(solLum) stat;phys.luminosity
logm_best log(M) best multivariate maximum posterior estimate for the log10 of the mass. log(solMass) phys.mass
logm_p50 log(M) med median of the distribution of log10 of the mass. log(solMass) phys.mass;stat.median
logm_dist P(log(M)) Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the mass. In ADQL, write logm_dist[1] for the minimum, logm_dist[2] for the 16th percentile, and so on. log(solMass) stat;phys.mass
logt_best log(T_eff) best multivariate maximum posterior estimate for the log10 of the effective temperature. log(K) phys.temperature
logt_p50 log(T_eff) med median of the distribution of log10 of the effective temperature. log(K) phys.temperature;stat.median
logt_dist P(log(T_eff)) Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the effective temperature. In ADQL, write logt_dist[1] for the minimum, logt_dist[2] for the 16th percentile, and so on. log(K) stat;phys.temperature
logg_best log(g) best multivariate maximum posterior estimate for the log10 of the surface gravity. log(cm/s**2) phys.gravity
logg_p50 log(g) med median of the distribution of log10 of the surface gravity. log(cm/s**2) phys.gravity;stat.median
logg_dist P(log(g)) Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the surface gravity. In ADQL, write logg_dist[1] for the minimum, logg_dist[2] for the 16th percentile, and so on. log(cm/s**2) stat;phys.gravity
a_bp_best A_BP best multivariate maximum posterior estimate for the attenuation in the Gaia BP band towards this source.. mag phys.absorption;em.opt.B
a_bp_p50 A_BP med median of the distribution of attenuation in the Gaia BP band towards this source.. mag phys.absorption;em.opt.B;stat.median
a_bp_dist P(A_BP) Distribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia BP band towards this source.. In ADQL, write a_bp_dist[1] for the minimum, a_bp_dist[2] for the 16th percentile, and so on. mag stat;phys.absorption;em.opt.B
a_g_best A_G best multivariate maximum posterior estimate for the attenuation in the Gaia G band towards this source.. mag phys.absorption;em.opt
a_g_p50 A_G med median of the distribution of attenuation in the Gaia G band towards this source.. mag phys.absorption;em.opt;stat.median
a_g_dist P(A_G) Distribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia G band towards this source.. In ADQL, write a_g_dist[1] for the minimum, a_g_dist[2] for the 16th percentile, and so on. mag stat;phys.absorption;em.opt
a_rp_best A_RP;em.opt.R best multivariate maximum posterior estimate for the attenuation in the Gaia RP band towards this source.. mag phys.absorption
a_rp_p50 A_RP;em.opt.R med median of the distribution of attenuation in the Gaia RP band towards this source.. mag phys.absorption;stat.median
a_rp_dist P(A_RP;em.opt.R) Distribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia RP band towards this source.. In ADQL, write a_rp_dist[1] for the minimum, a_rp_dist[2] for the 16th percentile, and so on. mag stat;phys.absorption
mag_bp BP orig Recalibrated Gaia BP magnitude. [Note gpc] mag phot.mag;em.opt.B
err_bp Err. BP orig Recalibrated error from original Gaia BP magnitude. [Note sc] mag stat.error;phot.mag;em.opt.B
bp_best BP best multivariate maximum posterior estimate for the Gaia BP magnitude. mag phot.mag;em.opt.B
bp_p50 BP med median of the distribution of Gaia BP magnitude. mag phot.mag;em.opt.B;stat.median
bp_dist P(BP) Distribution (min, p16, p25, p50, p75, p84, max) of the Gaia BP magnitude. In ADQL, write bp_dist[1] for the minimum, bp_dist[2] for the 16th percentile, and so on. mag stat;phot.mag;em.opt.B
mag_g G orig Recalibrated Gaia G magnitude. mag phot.mag;em.opt
err_g Err. G orig Recalibrated error from original Gaia G magnitude. [Note sc] mag stat.error;phot.mag;em.opt
g_best G best multivariate maximum posterior estimate for the Gaia G magnitude. mag phot.mag;em.opt
g_p50 G med median of the distribution of Gaia G magnitude. mag phot.mag;em.opt;stat.median
g_dist P(G) Distribution (min, p16, p25, p50, p75, p84, max) of the Gaia G magnitude. In ADQL, write g_dist[1] for the minimum, g_dist[2] for the 16th percentile, and so on. mag stat;phot.mag;em.opt
mag_rp RP orig Recalibrated Gaia RP magnitude. [Note gpc] mag phot.mag;em.opt.R
err_rp Err. RP orig Recalibrated error from original Gaia RP magnitude. [Note sc] mag stat.error;phot.mag;em.opt.R
rp_best RP best multivariate maximum posterior estimate for the Gaia RP magnitude. mag phot.mag;em.opt.R
rp_p50 RP med median of the distribution of Gaia RP magnitude. mag phot.mag;em.opt.R;stat.median
rp_dist P(RP) Distribution (min, p16, p25, p50, p75, p84, max) of the Gaia RP magnitude. In ADQL, write rp_dist[1] for the minimum, rp_dist[2] for the 16th percentile, and so on. mag stat;phot.mag;em.opt.R
mag_j J orig Recalibrated 2MASS J magnitude. mag phot.mag;em.IR.J
err_j Err. J orig Recalibrated error from original 2MASS J magnitude. [Note sc] mag stat.error;phot.mag;em.IR.J
j_best J best multivariate maximum posterior estimate for the 2MASS J magnitude. mag phot.mag;em.IR.J
j_p50 J med median of the distribution of 2MASS J magnitude. mag phot.mag;em.IR.J;stat.median
j_dist P(J) Distribution (min, p16, p25, p50, p75, p84, max) of the 2MASS J magnitude. In ADQL, write j_dist[1] for the minimum, j_dist[2] for the 16th percentile, and so on. mag stat;phot.mag;em.IR.J
mag_h H orig Recalibrated 2MASS H magnitude. mag phot.mag;em.IR.H
err_h Err. H orig Recalibrated error from original 2MASS H magnitude. [Note sc] mag stat.error;phot.mag;em.IR.H
h_best H best multivariate maximum posterior estimate for the 2MASS H magnitude. mag phot.mag;em.IR.H
h_p50 H med median of the distribution of 2MASS H magnitude. mag phot.mag;em.IR.H;stat.median
h_dist P(H) Distribution (min, p16, p25, p50, p75, p84, max) of the 2MASS H magnitude. In ADQL, write h_dist[1] for the minimum, h_dist[2] for the 16th percentile, and so on. mag stat;phot.mag;em.IR.H
mag_ks K orig Recalibrated 2MASS Ks magnitude. mag phot.mag;em.IR.K
err_ks Err. K orig Recalibrated error from original 2MASS Ks magnitude. [Note sc] mag stat.error;phot.mag;em.IR.K
ks_best K best multivariate maximum posterior estimate for the 2MASS Ks magnitude. mag phot.mag;em.IR.K
ks_p50 K med median of the distribution of 2MASS Ks magnitude. mag phot.mag;em.IR.K;stat.median
ks_dist P(K) Distribution (min, p16, p25, p50, p75, p84, max) of the 2MASS Ks magnitude. In ADQL, write ks_dist[1] for the minimum, ks_dist[2] for the 16th percentile, and so on. mag stat;phot.mag;em.IR.K
mag_w1 W1 orig Recalibrated WISE W1 magnitude. mag phot.mag;em.IR.3-4um
err_w1 Err. W1 orig Recalibrated error from original WISE W1 magnitude. [Note sc] mag stat.error;phot.mag;em.IR.3-4um
w1_best W1 best multivariate maximum posterior estimate for the WISE W1 magnitude. mag phot.mag;em.IR.3-4um
w1_p50 W1 med median of the distribution of WISE W1 magnitude. mag phot.mag;em.IR.3-4um;stat.median
w1_dist P(W1) Distribution (min, p16, p25, p50, p75, p84, max) of the WISE W1 magnitude. In ADQL, write w1_dist[1] for the minimum, w1_dist[2] for the 16th percentile, and so on. mag stat;phot.mag;em.IR.3-4um
mag_w2 W2 orig Recalibrated WISE W2 magnitude. mag phot.mag;em.IR.4-8um
err_w2 Err. W2 orig Recalibrated error from original WISE W2 magnitude. [Note sc] mag stat.error;phot.mag;em.IR.4-8um
w2_best W2 best multivariate maximum posterior estimate for the WISE W2 magnitude. mag phot.mag;em.IR.4-8um
w2_p50 W2 med median of the distribution of WISE W2 magnitude. mag phot.mag;em.IR.4-8um;stat.median
w2_dist P(W2) Distribution (min, p16, p25, p50, p75, p84, max) of the WISE W2 magnitude. In ADQL, write w2_dist[1] for the minimum, w2_dist[2] for the 16th percentile, and so on. mag stat;phot.mag;em.IR.4-8um
dmod_best DM best multivariate maximum posterior estimate for the distance modulus. mag phot.mag.distMod
dmod_p50 DM med median of the distribution of distance modulus. mag phot.mag.distMod;stat.median
dmod_dist P(DM) Distribution (min, p16, p25, p50, p75, p84, max) of the distance modulus. In ADQL, write dmod_dist[1] for the minimum, dmod_dist[2] for the 16th percentile, and so on. mag stat;phot.mag.distMod
lnlike_best ln(P) best multivariate maximum posterior estimate for the log likelihood of the solution (paper Eq. 1). N/A stat.param
lnlike_p50 ln(P) med median of the distribution of log likelihood of the solution (paper Eq. 1). N/A stat.param;stat.median
lnlike_dist P(ln(P)) Distribution (min, p16, p25, p50, p75, p84, max) of the log likelihood of the solution (paper Eq. 1). In ADQL, write lnlike_dist[1] for the minimum, lnlike_dist[2] for the 16th percentile, and so on. N/A stat;stat.param
lnp_best ln(Pp) best multivariate maximum posterior estimate for the log posterior of the solution (paper Eq. 2). N/A stat.probability
lnp_p50 ln(Pp) med median of the distribution of log posterior of the solution (paper Eq. 2). N/A stat.probability;stat.median
lnp_dist P(ln(Pp)) Distribution (min, p16, p25, p50, p75, p84, max) of the log posterior of the solution (paper Eq. 2). In ADQL, write lnp_dist[1] for the minimum, lnp_dist[2] for the 16th percentile, and so on. N/A stat;stat.probability
log10jitter_best log(jitter) best multivariate maximum posterior estimate for the log photometric likelihood jitter common to all bands. log(mag) stat.param
log10jitter_p50 log(jitter) med median of the distribution of log photometric likelihood jitter common to all bands. log(mag) stat.param;stat.median
log10jitter_dist P(log(jitter)) Distribution (min, p16, p25, p50, p75, p84, max) of the log photometric likelihood jitter common to all bands. In ADQL, write log10jitter_dist[1] for the minimum, log10jitter_dist[2] for the 16th percentile, and so on. log(mag) stat;stat.param
parallax Parallax Recalibrated parallax. mas pos.parallax
err_parallax Err. Parallax Error in recalibrated parallax. [Note epc] mas stat.error;pos.parallax

Columns that are parts of indices are marked like this.

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Notes