J-PLUS: stellar parameter determination of members of open clusters using machine learning

ABSTRACT We present a machine learning approach to estimate stellar atmospheric parameters ($T_\mathrm{eff}$, $\log \, g$, and $[\mathrm{Fe}/\mathrm{H}]$) for stars in the J-PLUS DR3 footprint using photometric and astrometric data from J-PLUS, Gaia, and CatWISE. Our models are based on the gradient boosting algorithm LightGBM and were trained using extinction-corrected photometry and absolute magnitudes derived from Gaia parallaxes. Through a systematic feature selection process, we identified the most informative features for each parameter, showing that using the full set of selected features yields the best overall performance. Evaluation against spectroscopic values from LAMOST DR8 reveals that our models achieve competitive mean absolute errors of 42 K, 0.06 dex, and 0.06 dex for $T_\mathrm{eff}$, $\log \, g$, and $[\mathrm{Fe}/\mathrm{H}]$, respectively. We applied the trained models to a curated sample of 154 stars belonging to open clusters and moving groups. While our metallicity estimates are mildly but systematically underestimated by $-0.13$ dex, we attribute this to the compression of training data in a compact and non-linear region of the feature space. Despite this bias, the relative metallicity patterns among clusters remain consistent with literature values, demonstrating the robustness of our predictions for comparative studies. Our method provides a reliable alternative for estimating stellar parameters using multiband photometry combined with astrometric information, offering further applications to surveys such as S-PLUS and J-PAS. Future improvements for parameter predictions will focus on additional magnitude combinations, predicting other parameters such as $[\mathrm{\alpha }/\mathrm{Fe}]$, and techniques for cluster member selection.

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Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2026-01-21
DOI
https://doi.org/10.1093/mnras/stag132
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
0.00

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article

J-PLUS: stellar parameter determination of members of open clusters using machine learning

A. Álvarez-Candal, F. Jiménez-Esteban, C. López-Sanjuán, D. Cristóbal-Hornillos et al.
Monthly Notices of the Royal Astronomical Society
Stellar, planetary, and galactic studies
article

J-PLUS: stellar parameter determination of members of open clusters using machine learning

A. Álvarez-Candal, F. Jiménez-Esteban, C. López-Sanjuán, D. Cristóbal-Hornillos, E. Machado-Pereira, Borja Anguiano, R. A. Dupke, Sarah Dario Alves Daflon, C. Bonatto, Mariano Moles, P Cruz, V M Placco, H Vázquez Ramió, A Marín-Franch, C Hernández-Monteagudo, A Ederoclite, L Sodré, J Varela, H. Yuan, J Cenarro, J Alcaniz, R E Angulo, J A Caballero, M Borges Fernandes
article en

Abstract

ABSTRACT We present a machine learning approach to estimate stellar atmospheric parameters ($T_\mathrm{eff}$, $\log \, g$, and $[\mathrm{Fe}/\mathrm{H}]$) for stars in the J-PLUS DR3 footprint using photometric and astrometric data from J-PLUS, Gaia, and CatWISE. Our models are based on the gradient boosting algorithm LightGBM and were trained using extinction-corrected photometry and absolute magnitudes derived from Gaia parallaxes. Through a systematic feature selection process, we identified the most informative features for each parameter, showing that using the full set of selected features yields the best overall performance. Evaluation against spectroscopic values from LAMOST DR8 reveals that our models achieve competitive mean absolute errors of 42 K, 0.06 dex, and 0.06 dex for $T_\mathrm{eff}$, $\log \, g$, and $[\mathrm{Fe}/\mathrm{H}]$, respectively. We applied the trained models to a curated sample of 154 stars belonging to open clusters and moving groups. While our metallicity estimates are mildly but systematically underestimated by $-0.13$ dex, we attribute this to the compression of training data in a compact and non-linear region of the feature space. Despite this bias, the relative metallicity patterns among clusters remain consistent with literature values, demonstrating the robustness of our predictions for comparative studies. Our method provides a reliable alternative for estimating stellar parameters using multiband photometry combined with astrometric information, offering further applications to surveys such as S-PLUS and J-PAS. Future improvements for parameter predictions will focus on additional magnitude combinations, predicting other parameters such as $[\mathrm{\alpha }/\mathrm{Fe}]$, and techniques for cluster member selection.

Monthly Notices of the Royal Astronomical SocietyVol. 546(3)
Ikerbasque (ES), Universidade Federal do Rio Grande do Sul (BR), Beijing Normal University (CN), Valongo Observatory (BR), Centro de Estudios de Física del Cosmos de Aragón (ES), Centro de Astrobiología (ES), Community Science and Data Center (US), NSF NOIRLab (US), Instituto de Geofísica y Astronomía (CU), Instituto de Astrofísica de Andalucía (ES), Donostia International Physics Center (ES), University of Virginia (US)
National Science Foundation, National Aeronautics and Space Administration, Ministerio de Ciencia, Innovación y Universidades, European Commission, Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Ministerio de Economía y Competitividad, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, Ministerio de Ciencia e Innovación, Jiangsu Association for Science and Technology, Agencia Estatal de Investigación
Openalex Percentile: Top 79%
Stellar, planetary, and galactic studies
DOIOpen Access PDFOpenAlex
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