CoSHA: Code for Stellar properties Heuristic Assignment -- for the MaStar stellar library
Alfredo Mej\'ia-Narv\'aez, Gustavo Bruzual, Sebastian F. S\'anchez,, Leticia Carigi, Jorge Barrera-Ballesteros, Mabel Valerdi, Renbin Yan, Niv, Drory

TL;DR
extit{CoSHA} is a machine learning-based method that estimates stellar atmospheric parameters for thousands of stars in the MaStar library, achieving accuracy comparable to existing methods and revealing known galactic trends.
Contribution
The paper introduces extit{CoSHA}, a novel gradient boosting algorithm for estimating stellar properties across large stellar libraries, validated with internal and external tests.
Findings
Parameter estimates cover wide stellar ranges.
Uncertainties are comparable to existing methods.
Main galactic trends are recovered.
Abstract
We introduce \cosha{}: a Code for Stellar properties Heuristic Assignment. In order to estimate the stellar properties, \cosha{} implements a Gradient Tree Boosting algorithm to label each star across the parameter space (, , , and ). We use \cosha{} to estimate these stellar atmospheric parameters of k unique stars in the MaNGA Stellar Library (MaStar). To quantify the reliability of our approach, we run both internal tests using the G\"ottingen Stellar Library (GSL, a theoretical library) and the first data release of MaStar, and external tests by comparing the resulting distributions in the parameter space with the APOGEE estimates of the same properties. In summary, our parameter estimates span in the ranges: K, , ,…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Cholesterol and Lipid Metabolism · Astronomy and Astrophysical Research
