{"paper":{"title":"The miniJPAS survey: star-galaxy classification using machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"A. Ederoclite, A. J. Cenarro, A. L. de Amorim, A. Mar\\'in-Franch, C. Hern\\'andez-Monteagudo, C. L\\'opez-Sanjuan, C. M. de Oliveira, C. Queiroz, D. Crist\\'obal-Hornillos, D. Muniesa, D. Sobral, E. Solano, E. Tempel, H. V\\'azquez Rami\\'o, J. Alcaniz, J. M. V\\'ilchez, J. Varela, K. Taylor, L. A. D\\'iaz-Garc\\'ia, L. Casarini, L. Sodr\\'e, M. Moles, M. Quartin, N. Benitez, P. A. A. Lopes, P. O. Baqui, R. Abramo, R. Angulo, R. Dupke, R. M. Gonz\\'alez Delgado, S. Bonoli, S. Carneiro, V. Marra, V. M. Placco","submitted_at":"2020-07-15T11:26:39Z","abstract_excerpt":"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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.07622","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2007.07622/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}