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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. |
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