Galaxies, Vol. 14, Pages 6: The miniJPAS and J-NEP Surveys: Machine Learning for Star-Galaxy Separation

Galaxies doi: 10.3390/galaxies14010006

Authors:
Ana Paula Jeakel
Gabriel Vieira dos Santos
Valerio Marra
Rodrigo von Marttens
Siddhartha Gurung-López
Raul Abramo
Jailson Alcaniz
Narciso Benitez
Silvia Bonoli
Javier Cenarro
David Cristóbal-Hornillos
Simone Daflon
Renato Dupke
Alessandro Ederoclite
Rosa M. González Delgado
Antonio Hernán-Caballero
Carlos Hernández-Monteagudo
Jifeng Liu
Carlos López-Sanjuan
Antonio Marín-Franch
Claudia Mendes de Oliveira
Mariano Moles
Fernando Roig
Laerte Sodré
Keith Taylor
Jesús Varela
Héctor Vázquez Ramió
José M. Vilchez
Christopher Willmer
Javier Zaragoza-Cardiel

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 14,594 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 Å, 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.

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Ana Paula Jeakel www.mdpi.com