This work presents a methodology for characterizing stellar objects using photometric data obtained from the J-PLUS mission in its third version. The J-PLUS mission does not provide direct astrophysical parameters such as effective temperature and luminosity, which limits our understanding of the stars’ evolutionary state. To overcome this limitation, we perform a data cross-matching with the GAIA DR3 space mission, which offers detailed information on these properties.

By conducting the data cross-matching, we obtain effective temperature and luminosity labels for certain stellar objects in the J-PLUS DR3 catalog. These labels are then utilized as training data for supervised machine learning techniques, specifically the XGBoost algorithm for estimating effective temperature and the Random Forest algorithm for estimating the luminosity of the missing stellar objects in the J-PLUS catalog.

The integration of J-PLUS and GAIA data, combined with the application of supervised learning algorithms, enables accurate and efficient estimation of the missing astrophysical parameters in the J-PLUS DR3 catalog. This methodology significantly contributes to the characterization of the 8,013,455 stars observed by J-PLUS DR3.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Luis H. Sánchez, Ana V. Ojeda, Juán J. Tapia, and C. A. Guerrero, "Estimation of stellar parameters in J-PLUS DR3 through machine learning techniques," Proc. SPIE 13136, Optics and Photonics for Information Processing XVIII, 131360C (Presented at Optical Engineering + Applications: August 21, 2024; Published: 30 September 2024); https://doi.org/10.1117/12.3028734.