The Survey of Surveys: machine learning for stellar parametrization

TL;DR

This paper introduces a machine learning approach that leverages homogenized spectroscopic data to accurately estimate stellar parameters from photometric surveys, enabling spectroscopic-quality results for millions of stars.

Contribution

It presents a novel neural network method that uses homogenized spectroscopic data to derive stellar parameters from photometry, improving accuracy and applicability.

Findings

Achieves uncertainties of ~100K in temperature, 0.1 dex in gravity, 0.1 dex in metallicity.

Performs well even at low metallicities, where data is typically challenging.

Provides spectroscopic-quality parameters for millions of stars from photometric data.

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.