J/AJ/158/93 Regression of stellar effective temperatures in GaiaDR2 (Bai+, 2019)
Machine-learning regression of stellar effective temperatures in the second
Gaia data release.
Bai Y., Liu J., Bai Z., Wang S., Fan D.
<Astron. J., 158, 93-93 (2019)>
=2019AJ....158...93B 2019AJ....158...93B (SIMBAD/NED BibCode)
ADC_Keywords: Stellar distribution ; Effective temperatures
Keywords: methods: data analysis - stars: fundamental parameters -
techniques: spectroscopic
Abstract:
This paper reports on the application of the supervised machine-learning
algorithm to the stellar effective temperature regression for the second
Gaia data release (Cat. I/345), based on the combination of the stars in
four spectroscopic surveys: the Large Sky Area Multi-Object Fiber
Spectroscopic Telescope, Sloan Extension for Galactic Understanding and
Exploration, the Apache Point Observatory Galactic Evolution Experiment,
and the Radial Velocity Extension. This combination, of about four million
stars, enables us to construct one of the largest training samples for
the regression and further predict reliable stellar temperatures with
a rms error of 191 K. This result is more precise than that given by
the Gaia second data release that is based on about sixty thousands stars.
After a series of data cleaning processes, the input features that feed
the regressor are carefully selected from the Gaia parameters, including
the colors, the 3D position, and the proper motion. These Gaia parameters
are used to predict effective temperatures for 132739323 valid stars
in the second Gaia data release. We also present a new method for blind
tests and a test for external regression without additional data. The
machine-learning algorithm fed with the parameters only in one catalog
provides us with an effective approach to maximize the sample size for
prediction, and this methodology has a wide application prospect in
future studies of astrophysics.
Description:
The Teff for Gaia DR2 stars (Cat. I/345) was determined using two colors
in the Gaia photometric bands, G-GRP and GBP-G (Andrae et al. 2018,
Cat. I/345), using a supervised machine-learning algorithm. The criteria
below are used to select the samples in Gaia DR2 (132739323 stars):
ΔTeff/Teff<0.05, 0<Δω/ω<0.2,
G-GRP=<1.79.log10(GBP-G+0.42)+0.71+0.15,
G-GRP≥1.79.log10(GBP-G+0.42)+0.71-0.15. The result is listed in
Table 2.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table2.sam 61 1000 Results of our regression in Gaia DR2
(Cat. I/345)
table2.txt 72 132739322 Original table from paper, with sources and
temperatures
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See also:
I/345 : Gaia DR2 (Gaia Collaboration, 2018)
Byte-by-byte Description of file: table2.sam
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 19 I19 --- Source Source identifier in Gaia DR2 (Gaia)
21- 35 F15.11 deg RAdeg Gaia DR2 Right Ascension (ICRS) at Ep=2015.5
(column added by the CDS)
37- 51 F15.11 deg DEdeg Gaia DR2 Declination (ICRS) at Ep=2015.5
(column added by the CDS)
53- 56 I4 K Teff [3893/8871] Regressed effective temperature
(Teff)
58- 61 I4 K e_Teff [9/1098] Uncertainty in Teff (e_Teff)
--------------------------------------------------------------------------------
Description of file: table2.txt
-------------------------------------------------------------------------------
Bytes Format Units Label Explanations
-------------------------------------------------------------------------------
1- 19 I19 --- Source Source identifier in Gaia DR2 (Gaia)
21- 24 I4 K Teff [3893/8871] Regressed effective temperature
(Teff)
26- 29 I4 K e_Teff [9/1098] Uncertainty in Teff (e_Teff)
31- 50 A20 deg RAdeg Gaia DR2 Right Ascension (ICRS) at Ep=2015.5
(column added by the CDS)
52- 72 A21 deg DEdeg Gaia DR2 Declination (ICRS) at Ep=2015.5
(column added by the CDS)
-------------------------------------------------------------------------------
History:
From electronic version of the journal
(End) Prepared by [AAS], T. Pouvreau, F.-X. Pineau [CDS] 10-Oct-2019