TL;DR
This study develops machine learning-based empirical relations to accurately estimate the metallicity of RR Lyrae stars from their I-band light curves, improving previous biases and enabling detailed stellar population analysis.
Contribution
The paper introduces new Bayesian regression models for metallicity prediction from I-band light curves, reducing systematic biases and providing the first large-scale photometric MDFs for multiple stellar systems.
Findings
Achieved mean absolute prediction errors of 0.16 and 0.18 dex for RRab and RRc stars.
Corrected previous positive biases in metallicity estimates, especially in metal-poor regimes.
Derived MDF modes consistent with spectroscopic results for the Milky Way bulge and nearby dwarf galaxies.
Abstract
We have revisited the problem of metallicity prediction of RR Lyrae stars from their near-infrared light curves in the Cousins I waveband. Our study is based on high-quality time-series photometry and state-of-the-art high-resolution spectroscopic abundance measurements of 80 fundamental-mode (RRab) and 24 first-overtone (RRc) stars, spanning [,] dex and [,] dex ranges, respectively. Employing machine-learning methods, we investigated various light-curve representations and regression models to identify their optimal form for our objective. Accurate new empirical relations between the [Fe/H] iron abundance and the light-curve parameters have been obtained using Bayesian regression for both RRab and RRc stars with mean absolute prediction errors of 0.16 and 0.18 dex, respectively. We found that earlier -band [Fe/H] estimates had a systematic positive…
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