We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and photometric catalogs, we construct a robust labeled dataset comprising 14594 sources classified into extended (galaxies) and point-like (stars and quasars) objects. We assess dataset representativeness using UMAP analysis, confirming broad and consistent coverage of feature space. An XGBoost classifier, with hyperparameters tuned using automated optimization, is trained using purely photometric data (60-band J-PAS magnitudes) and combined photometric and morphological features, with performance thoroughly evaluated via ROC and purity-completeness metrics. Incorporating morphology significantly improves classification, outperforming the baseline classifications available in the catalogs. Permutation importance analysis reveals morphological parameters, particularly concentration, normalized peak surface brightness, and PSF, alongside photometric features around 4000 and 6900 A, as crucial for accurate classifications. We release a value-added catalog with our models for star-galaxy classification, enhancing the utility of miniJPAS and J-NEP for subsequent cosmological and astrophysical analyses.
@article{arxiv.2511.20524,
title = {The miniJPAS and J-NEP surveys: Machine learning for star-galaxy separation},
author = {Ana Paula Jeakel and Gabriel Vieira dos Santos and Valerio Marra and Rodrigo von Marttens and Siddhartha Gurung-López and Raul Abramo and Jailson Alcaniz and Narciso Benitez and Silvia Bonoli and Javier Cenarro and David Cristóbal-Hornillos and Simone Daflon and Renato Dupke and Alessandro Ederoclite and Rosa M. González Delgado and Antonio Hernán-Caballero and Carlos Hernández-Monteagudo and Jifeng Liu and Carlos López-Sanjuan and Antonio Marín-Franch and Claudia Mendes de Oliveira and Mariano Moles and Fernando Roig and Laerte Sodré and Keith Taylor and Jesús Varela and Héctor Vázquez Ramió and José M. Vilchez and Christopher Willmer and Javier Zaragoza-Cardiel},
journal= {arXiv preprint arXiv:2511.20524},
year = {2026}
}