The miniJPAS survey : star-galaxy classification using machine learning

Baqui, P. O. and Marra, V. and Casarini, L. and Angulo, R. and Díaz-García, L. A. and Hernández-Monteagudo, C. and Lopes, P. A. A. and López-Sanjuan, C. and Muniesa, D. and Placco, V. M. and Quartin, M. and Queiroz, C. and Sobral, D. and Solano, E. and Tempel, E. and Varela, J. and Vílchez, J. M. and Abramo, R. and Alcaniz, J. and Benitez, N. and Bonoli, S. and Carneiro, S. and Cenarro, J. and Cristóbal-Hornillos, D. and Amorim, A. L. de and Oliveira, C. M. de and Dupke, R. and Ederoclite, A. and Delgado, R. M. González and Marín-Franch, A. and Moles, M. and Ramió, H. Vázquez and Sodré, L. (2021) The miniJPAS survey : star-galaxy classification using machine learning. Astronomy and Astrophysics, 645: A87. ISSN 1432-0746

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Abstract

Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about 1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools based on explicit modeling. In order to train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS and HSC-SSP data. We trained and tested 6 different ML algorithms on the two crossmatched catalogs. As input for the ML algorithms we use the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 1521). We use our best classifiers, with and without morphology, in order to produce a value added catalog available at https://j-pas.org/datareleases .