{"paper":{"title":"The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.GA","authors_text":"A. Hern\\'an-Caballero, A. Javier Cenarro, Alessandro Ederoclite, Antonio Mar\\'in-Franch, Carlos L\\'opez-Sanjuan, Carolina Queiroz, Claudia Mendes de Oliveira, Gin\\'es Mart\\'inez-Solaeche, H\\'ector V\\'azquez Rami\\'o, Ignasi P\\'erez-R\\`afols, Isabel M\\'arquez, Jes\\'us Varela, Jon\\'as Chaves-Montero, Keith Taylor, L.A. D\\'iaz-Garc\\'ia, Laerte Sodr\\'e Jr., L. Raul Abramo, Mariano Moles, Matthew M. Pieri, Narciso Ben\\'itez, Nat\\'alia V.N. Rodrigues, Renato A. Dupke, Rosa M. Gonz\\'alez Delgado, Sean S. Morrison, Silvia Bonoli, Valerio Marra","submitted_at":"2023-03-01T13:25:09Z","abstract_excerpt":"Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a Machine Learning-based method that employs Convolutional Neural Networks (CNNs) to classify point-like sources including the information in the measuremen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.00489","kind":"arxiv","version":1},"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/2303.00489/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"}