The survey of surveys: machine learning for stellar parametrization

A. Turchi; E. Pancino; F. Rossi; A. Avdeeva; P. Marrese; S. Marinoni; N. Sanna; M. Tsantaki; G. Fanari

doi:10.1117/12.3018967

25 July 2024 The survey of surveys: machine learning for stellar parametrization

A. Turchi, E. Pancino, F. Rossi, A. Avdeeva, P. Marrese, S. Marinoni, N. Sanna, M. Tsantaki, G. Fanari

Proceedings Volume 13101, Software and Cyberinfrastructure for Astronomy VIII; 131010Z (2024) https://doi.org/10.1117/12.3018967
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

A. Turchi, E. Pancino, F. Rossi, A. Avdeeva, P. Marrese, S. Marinoni, N. Sanna, M. Tsantaki, and G. Fanari "The survey of surveys: machine learning for stellar parametrization", Proc. SPIE 13101, Software and Cyberinfrastructure for Astronomy VIII, 131010Z (25 July 2024); https://doi.org/10.1117/12.3018967

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