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| Natura: | Preprint |
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2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.20524 |
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| author | Jeakel, Ana Paula Santos, Gabriel Vieira dos Marra, Valerio von Marttens, Rodrigo Gurung-López, Siddhartha Abramo, Raul Alcaniz, Jailson Benitez, Narciso Bonoli, Silvia Cenarro, Javier Cristóbal-Hornillos, David Daflon, Simone Dupke, Renato Ederoclite, Alessandro Delgado, Rosa M. González Hernán-Caballero, Antonio Hernández-Monteagudo, Carlos Liu, Jifeng López-Sanjuan, Carlos Marín-Franch, Antonio de Oliveira, Claudia Mendes Moles, Mariano Roig, Fernando Sodré Jr., Laerte Taylor, Keith Varela, Jesús Ramió, Héctor Vázquez Vilchez, José M. Willmer, Christopher Zaragoza-Cardiel, Javier |
| author_facet | Jeakel, Ana Paula Santos, Gabriel Vieira dos Marra, Valerio von Marttens, Rodrigo Gurung-López, Siddhartha Abramo, Raul Alcaniz, Jailson Benitez, Narciso Bonoli, Silvia Cenarro, Javier Cristóbal-Hornillos, David Daflon, Simone Dupke, Renato Ederoclite, Alessandro Delgado, Rosa M. González Hernán-Caballero, Antonio Hernández-Monteagudo, Carlos Liu, Jifeng López-Sanjuan, Carlos Marín-Franch, Antonio de Oliveira, Claudia Mendes Moles, Mariano Roig, Fernando Sodré Jr., Laerte Taylor, Keith Varela, Jesús Ramió, Héctor Vázquez Vilchez, José M. Willmer, Christopher Zaragoza-Cardiel, Javier |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20524 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The miniJPAS and J-NEP surveys: Machine learning for star-galaxy separation Jeakel, Ana Paula Santos, Gabriel Vieira dos Marra, Valerio von Marttens, Rodrigo Gurung-López, Siddhartha Abramo, Raul Alcaniz, Jailson Benitez, Narciso Bonoli, Silvia Cenarro, Javier Cristóbal-Hornillos, David Daflon, Simone Dupke, Renato Ederoclite, Alessandro Delgado, Rosa M. González Hernán-Caballero, Antonio Hernández-Monteagudo, Carlos Liu, Jifeng López-Sanjuan, Carlos Marín-Franch, Antonio de Oliveira, Claudia Mendes Moles, Mariano Roig, Fernando Sodré Jr., Laerte Taylor, Keith Varela, Jesús Ramió, Héctor Vázquez Vilchez, José M. Willmer, Christopher Zaragoza-Cardiel, Javier Instrumentation and Methods for Astrophysics 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. |
| title | The miniJPAS and J-NEP surveys: Machine learning for star-galaxy separation |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2511.20524 |