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The Survey of Surveys: machine learning for stellar parametrization

Journal
Date Issued
2024
Author(s)
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Fanari, Giorgio
DOI
Abstract
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in homogenizing and recalibrating spectroscopic data from surveys like APOGEE, GALAH, or LAMOST into a single catalog, which is used to inform a neural network. We obtain spectroscopic-quality parameters for millions of stars that have only been observed photometrically. The typical uncertainties are of the order of 100K in temperature, 0.1 dex in surface gravity, and 0.1 dex in metallicity and the method performs well down to low metallicity, were obtaining reliable results is known to be difficult.
Coverage
Software and Cyberinfrastructure for Astronomy VIII
All editors
Ibsen, Jorge; Chiozzi, Gianluca
Volume
13101
Start page
35
Conferenece
Software and Cyberinfrastructure for Astronomy VIII
Conferenece place
Yokohama, Japan
Conferenece date
16-22 June, 2024
Uri
Url
Issn Identifier
0277-786X
Rights
open.access
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