J-PLUS: Stellar Parameters, C, N, Mg, Ca and [{\alpha}/Fe] Abundances for Two Million Stars from DR1
Lin Yang, Haibo Yuan, Maosheng Xiang, Fuqing Duan, Yang Huang, Jifeng, Liu, Timothy C. Beers, Carlos Andr\'es Galarza, Simone Daflon, J.A., Fern\'andez-Ontiveros, Javier Cenarro, David Crist\'obal-Hornillos, Carlos, Hern\'andez-Monteagudo, Carlos L\'opez-Sanjuan

TL;DR
This study develops a neural network-based method to derive stellar parameters and elemental abundances from J-PLUS photometry, enabling detailed chemo-dynamic analysis of two million stars in the Milky Way.
Contribution
We introduce a cost-sensitive neural network approach to accurately estimate stellar parameters and elemental abundances from photometric data, validated against spectroscopic catalogs.
Findings
Achieved precisions of ~55K in Teff, 0.15 dex in log g, and 0.07 dex in [Fe/H]
Estimated [{eta}/Fe] and four elemental abundances with 0.04-0.08 dex accuracy
Produced a catalog for about two million stars for Galactic studies
Abstract
Context. The Javalambre Photometric Local Universe Survey (J-PLUS) has obtained precise photometry in twelve specially designed filters for large numbers of Galactic stars. Deriving their precise stellar atmospheric parameters and individual elemental abundances is crucial for studies of Galactic structure, and the assembly history and chemical evolution of our Galaxy. Aims. Our goal is to estimate not only stellar parameters (effective temperature, Teff, surface gravity, log g, and metallicity, [Fe/H]), but also [{\alpha}/Fe] and four elemental abundances ([C/Fe], [N/Fe], [Mg/Fe], and [Ca/Fe]) using data from J-PLUS DR1. Methods. By combining recalibrated photometric data from J-PLUS DR1, Gaia DR2, and spectroscopic labels from LAMOST, we design and train a set of cost-sensitive neural networks, the CSNet, to learn the non-linear mapping from stellar colors to their labels. Results. We…
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