Metallicity of RR Lyrae stars from the Gaia Data Release 3 catalogue computed with Machine Learning algorithms
Tatiana Muraveva, Andrea Giannetti, Gisella Clementini, Alessia, Garofalo, Lorenzo Monti

TL;DR
This study develops new machine learning-based relations to accurately determine the metallicity of RR Lyrae stars from Gaia DR3 data, enabling large-scale metallicity and distance measurements in the Milky Way and nearby galaxies.
Contribution
Introduces new $P-_{31}-{ m [Fe/H]}$ and $P-_{31}-A_2-{ m [Fe/H]}$ relations calibrated with Gaia DR3 data and spectroscopic metallicities, using Bayesian fitting and feature selection.
Findings
Achieved metallicity prediction errors of 0.28 and 0.21 dex for RRab and RRc stars.
Measured metallicities and distances for 134,000 RRLs and globular clusters.
Estimated LMC and SMC metallicities and distances consistent with previous studies.
Abstract
We present new and relations for fundamental-mode (RRab) and first-overtone mode (RRc) RR Lyrae stars (RRLs), respectively. The relations were calibrated based on pulsation periods and Fourier parameters of the RRL light curves in the Gaia -band published in the Gaia Data Release 3 (DR3), and accurate spectroscopically measured metallicities available in the literature. We apply the feature selection algorithm to identify the most relevant parameters for the determination of metallicity. To fit the relations, we used the Bayesian approach, which allowed us to carefully take into account uncertainties in various parameters and the intrinsic scatter of the relations. The root mean squared errors of the predicted metallicity values in the training samples are 0.28 dex and 0.21 dex for RRab and RRc stars, respectively,…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Astronomical Observations and Instrumentation · Stellar, planetary, and galactic studies
