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]

Name	Table Head	Description	Unit	UCD
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

Note p
Parallaxes have been re-calibrated by
- G<14: ϖ + 0.05 mas
- 14 ≤ G < 16: ϖ + (0.1676 − 0.0084 · G) mas
- 16 ≤ G: ϖ + (0.1676 − 0.0084 · G) mas
See paper, sect. 2 for details and references.
Note epc
Parallax error estimates have been re-calibrated by
- G<11: 1.2 · σ_ϖ
- 11 ≤ G < 15: (0.22 · G - 1.22) · σ_ϖ
- 15 ≤ G: (exp(15-G) + 1.08) · σ_ϖ
See paper, sect. 2 for details and references.
Note gpc
Gaia BP photometry has been re-calibrated according to the prescription of Maíz Apellániz & Weiler (2018), applying:
- the bright BP transmission curve for G < 10.5
- the faint BP transmission curve for 10.5 ≤ G
Gaia RP photometry is taken from Gaia DR2 directly (except possibly for adjusting the error estimate).

See paper, sect. 2 for details and references.
Note gg
Gaia G photometry has been re-calibrated according to the prescription of Maíz Apellániz & Weiler (2018):
- G < 6: G + 0.0271 · (6 − G)
- 6 ≤ G < 16: G − 0.0032 · (G − 6)
- 16 ≤ G: G − 0.032
See paper, sect. 2 for details and references.
Note sc
Where sources claimed magnitude errors below 0.05 mag, we assumed an error of 0.05 mag.

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