J/A+A/700/A195 Stellar parameters for 23 millions of stars (Turchi+, 2025)
Survey of Surveys. II. Stellar parameters for 23 millions of stars.
Turchi A., Pancino E., Avdeeva A., Rossi F., Tsantaki M., Marrese P.M.,
Marinoni S., Sanna N., Fanari G., Alvarez Garay D., Echeveste M.,
Nedhath S., Rani S., Reggiani E., Saracino S., Steinbauer L., Thomas G.,
Gran F., Guiglion G.
<Astron. Astrophys. 700, A195 (2025)>
=2025A&A...700A.195T 2025A&A...700A.195T (SIMBAD/NED BibCode)
ADC_Keywords: Surveys ; Abundances, [Fe/H]; Photometry; Effective temperatures;
Spectroscopy ; Optical
Keywords: methods: data analysis - methods: numerical -
techniques: spectroscopic - catalogs - surveys -
stars: fundamental parameters
Abstract:
In the current panorama of large surveys, the vast amount of data
obtained with different methods, data types, formats, and stellar
samples, is making an efficient use of the available information
difficult.
The Survey of Surveys is a project to critically compile survey
results in a single catalogue, facilitating the scientific use of the
available information. In this second release, we present two new
catalogs of stellar parameters (Teff, logg, and [Fe/H]).
To build the first catalog, SoS-Spectro, we calibrated internally and
externally stellar parameters from five spectroscopic surveys (APOGEE,
GALAH, Gaia-ESO, RAVE, and LAMOST). Our external calibration on the
PASTEL database of high-resolution spectroscopy ensures better
performances for metal-poor red giants. The second catalog, SoS-ML
catalog, is obtained by using SoS-Spectro as a reference to train a
multi-layer perceptron, which predicts stellar parameters based on two
photometric surveys, SDSS and SkyMapper. As a novel approach, we build
on previous parameters sets, from Gaia DR3 and Andrae et al.
(2023ApJS..267....8A 2023ApJS..267....8A), aiming to improve their precision and accuracy.
We obtain a catalog of stellar parameters for around 23 millions of
stars, which we make publicly available. We validate our results with
several comparisons with other machine learning catalogs, stellar
clusters, and astroseismic samples. We find substantial improvements
in the parameters estimates compared to other Machine Learning methods
in terms of precision and accuracy, especially in the metal-poor
range, as shown in particular when validating our results with
globular clusters. Conclusions. We believe that there are two reasons
behind our improved results at the low-metallicity end: first, our use
of a reference catalog "the SoS-Spectro" which is calibrated using
high-resolution spectroscopic data; and second, our choice to build on
pre-existing parameter estimates from Gaia and Andrae et al., rather
than attempting to obtain our predictions from survey data alone.
Description:
The catalog contains two major data sources. First the spectroscopic
Teff,logg and [Fe/H] for around 4.7 million stars, built using the
following spectroscopic surveys: APOGEE DR16 (Ahumadaet al.
2020ApJS..249....3A 2020ApJS..249....3A, Cat. V/154); GALAH DR2 (Buder et al.,
2018MNRAS.478.4513B 2018MNRAS.478.4513B, Cat. J/MNRAS/478/4513); Gaia-ESO DR3 (Gilmore et
al., 2012Msngr.147...25G 2012Msngr.147...25G); RAVE DR6 (Steinmetz et al.
2020AJ....160...83S 2020AJ....160...83S, Cat. III/283); LAMOST DR5 (Deng et al.
2012RAA....12..735D 2012RAA....12..735D, Cat. V/164/); and GAIA DR2 (Gaia Collaboration
2018, Cat. I/345) . These spectroscopic surveys were homogenized and
recalibrated within the SoS-Spectro effort (see Tsantaki et al.
2022A&A...659A..95T 2022A&A...659A..95T, Cat. J/A+A/659/A95, and the paper. SoS-Spectro
was already successfully used to characterize Landolt and Stetson
secondary standard stars (Pancino et al.,
2022A&A...664A.109P 2022A&A...664A.109P, Cat. J/A+A/664/A109), with excellent results even
for difficult parameters such as low log g or [Fe/H]. The second data
source is the Machine Learning (ML) generated Teff,logg and [Fe/H] for
around 19 million stars. This was done from Gaia DR3 photometry and
parameters crossmatched with SDSS DR13 (Albareti et al.,
2017ApJS..233...25A 2017ApJS..233...25A) and SkyMapper DR2 (Huang et al.
2021ApJ...907...68H 2021ApJ...907...68H) photometry, to predict stellar parameters with
SoS-Spectro as a reference. We describe the accuracy of these
parameters in the paper. Input metallicities come from Andrae et al.
2023ApJS..267....8A 2023ApJS..267....8A.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
catalog.dat 398 22726234 *SoS DR2 final catalog with cuts PASTEL
dr2 newSoSId reorder
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Note on catalog.dat: References as follows:
Tsantaki+22 = Tsantaki et al. (2022A&A...659A..95T 2022A&A...659A..95T)
Ahumada+20 = Ahumada et al. (2020ApJS..249....3A 2020ApJS..249....3A)
Buder+18 = Buder et al. (2018MNRAS.478.4513B 2018MNRAS.478.4513B)
Gilmore+12 = Gilmore et al. (2012Msngr.147...25G 2012Msngr.147...25G)
Steinmetz+20 = Steinmetz et al. (2020AJ....160...83S 2020AJ....160...83S)
Deng+12 = Deng et al. (2012RAA....12..735D 2012RAA....12..735D)
Abazajian+03 = Abazajian et al. (2003AJ....126.2081A 2003AJ....126.2081A)
Keller+07 = Keller et al. (2007PASA...24....1K 2007PASA...24....1K)
Brown+18 = Brown et al. (2018A&A...616A...1G 2018A&A...616A...1G)
Andrae+23 = Andrae et al. (2023ApJS..267....8A 2023ApJS..267....8A)
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See also:
I/345 : Gaia DR2 (Gaia Collaboration, 2018)
I/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)
II/379 : SkyMapper Southern Sky Survey. DR4 (Onken+, 2024)
III/283 : RAVE 6th data release (Steinmetz+, 2020)
V/154 : Sloan Digital Sky Surveys (SDSS), Release 16 (DR16)
(Ahumada+, 2020)
V/164 : LAMOST DR5 catalogs (Luo+, 2019)
J/MNRAS/478/4513 : GALAH Survey DR2 (Buder+, 2018)
J/A+A/664/A109 : Gaia EDR3 and Johnson-Kron-Cousins standards
(Pancino+, 2022)
Byte-by-byte Description of file: catalog.dat
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Bytes Format Units Label Explanations
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1- 14 I14 --- SoSID Unique SoS identifier (DR2)
16- 34 I19 --- GaiaDR2 ? Source_id from Gaia DR2, Cat. I/345
36- 54 I19 --- GaiaDR3 Source_id from Gaia DR3, Cat. I/355
56- 74 I19 --- SDSS ? ObjectId from SDSS, , Cat. V/154
76- 84 I9 --- SM ? ObjectId from SkyMapper, Cat. II/379
86-107 E22.20 deg RAdeg Right ascension (ICRS) at Ep=2016.0
from Gaia DR3
109-131 E23.20 deg DEdeg Declinatiop (ICRS) at Ep=2016.0
from Gaia DR3
133-151 F19.13 K specTeff ? SoS-Spectro effective temperature
153-169 F17.12 K e_specTeff ? Error on specTeff
171-189 E19.16 [cm/s2] speclogg ? SoS-Spectro gravity
191-205 E15.13 [cm/s2] e_speclogg ? Error on speclogg
207-229 E23.17 [-] spec[Fe/H] ? SoS-Spectro metallicity
231-245 E15.13 [-] e_spec[Fe/H] ? Error on spec_[Fe/H]
247-264 F18.13 K MLTeff ? Effective temperature from ML
266-284 F19.15 K e_MLTeff ? Error on MLTeff
286-304 F19.16 [cm/s2] MLlogg ? Gravity from ML
306-325 F20.18 [cm/s2] e_MLlogg ? Error on MLlogg
327-349 E23.20 [-] ML[Fe/H] ? Metallicity from ML
351-370 F20.18 [-] e_ML[Fe/H] ? Error on ML[Fe/H]
372-378 A7 --- Source ? ML source catalogue
(SDSS, SM, or SDSS+SM)
380-382 F3.1 --- SDSStrainset [0/1]? Star in SDSS training set
384-386 F3.1 --- SMtrainSet [0/1]? Star in SkyMapper training set
388-392 A5 --- SDSStrainarea [False True] Star in SDSS training area
394-398 A5 --- SMtrainarea [False True] Star in SkyMapper
training area
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Acknowledgements:
Alessio Turchi, alessio.turchi(at)inaf.it
(End) Patricia Vannier [CDS] 09-Jul-2025