J/AJ/159/84 Machine-learning regression of extinction in Gaia DR2 (Bai+, 2020)
Machine-learning regression of extinction in the second Gaia Data Release.
Bai Yu, Liu J., Wang Y., Wang S.
<Astron. J., 159, 84 (2020)>
=2020AJ....159...84B 2020AJ....159...84B
ADC_Keywords: Surveys; Extinction; Optical
Keywords: Interstellar dust extinction ; Analytical mathematics
Abstract:
Machine learning has become a popular tool to help us make better
decisions and predictions, based on experiences, observations, and
analyzing patterns, within a given data set without explicit
functions. In this paper, we describe an application of the supervised
machine-learning algorithm to the extinction regression for the second
Gaia data release, based on the combination of the Large Sky Area
Multi-Object Fiber Spectroscopic Telescope, Sloan Extension for
Galactic Understanding and Exploration, and the Apache Point
Observatory Galactic Evolution Experiment. The derived extinction in
our training sample is consistent with other spectrum-based estimates,
and its standard deviation of the cross-validations is 0.0127mag. A
blind test is carried out using the RAdial Velocity Experiment
catalog, and the standard deviation is 0.0372mag. Such a precise
training sample enables us to regress the extinction, E(BP-RP), for
133 million stars in the second Gaia data release. Of these,
106 million stars have the uncertainties less than 0.1mag, which
suffer less bias from the external regression. We also find that there
are high deviations between the extinctions from photometry-based
methods, and between spectrum- and photometry-based methods. This
implies that the spectrum-based method could bring more signal to a
regressing model than multiband photometry, and a higher
signal-to-noise ratio would acquire a more reliable result.
Description:
1.38 billion sources of the Gaia DR2 (Cat. I/345) have the integrate
fluxes from the blue and red photometer (BP and RP)
spectrophotometers, which span 3300-6800Å and 6400-10500Å,
respectively.
We apply the regressor and present a revised version of the E(BP-RP)
catalog for Gaia DR2.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.sam 71 1000 Results of our regression for Gaia DR2 (Cat. I/345)
(sample of 1000 sources among 132,739,322)
table2.txt 39 132739322 Original table from paper with IDs and regressed
extinctions
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See also:
III/279 : RAVE 5th data release (Kunder+, 2017)
I/345 : Gaia DR2 (Gaia Collaboration, 2018)
I/349 : StarHorse, Gaia DR2 photo-astrometric distances (Anders+, 2019)
V/146 : LAMOST DR1 catalogs (Luo+, 2015)
J/AJ/136/2070 : SEGUE stellar parameter pipeline. III (Allende Prieto+,2008)
J/AJ/136/2050 : SEGUE stellar parameter pipeline. II. (Lee+, 2008)
J/AJ/137/4377 : List of SEGUE plate pairs (Yanny+, 2009)
J/AJ/141/89 : SEGUE stellar parameter pipeline. IV. (Smolinski+, 2011)
J/AJ/146/133 : Stellar parameters SDSS-III APOGEE DR10 (Meszaros+, 2013)
J/ApJ/798/122 : Abundances SEGUE Stellar Parameters Pipeline (Miller+, 2015)
J/AJ/151/144 : ASPCAP weights 15 APOGEE chemical elements (Garcia+, 2016)
J/MNRAS/460/3179 : APOGEE stars distance and extinction (Wang+, 2016)
J/ApJS/229/30 : Revised stellar properties of Q1-17 Kepler (Mathur+, 2017)
J/A+A/619/A180 : Gaia DR2 photo. sensitivity curves (Maiz Apellaniz+, 2018)
J/AJ/158/93 : Regression of stellar temperatures in GaiaDR2 (Bai+, 2019)
Byte-by-byte Description of file: table2.sam
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Bytes Format Units Label Explanations
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1- 19 I19 --- Source Source identifier in Gaia DR2
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- 62 E10.3 mag E(BP-RP) [-0.46/2.91] Regressed redenning E(BP-RP)
64- 71 F8.6 mag e_E(BP-RP) [0.0024/0.87] Uncertainty in E(BP-RP)
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History:
From electronic version of the journal
(End) Prepared by [AAS], Coralie Fix [CDS], 09-Oct-2020