{"paper":{"title":"The miniJPAS survey quasar selection III: Classification with artificial neural networks and hybridisation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.GA","authors_text":"A. Ederoclite, A. Hern\\'an-Caballero, A.J. Cenarro, A. Mart\\'in-Franch, Carolina Queiroz, C. L\\'opez-Sanjuan, C. Mendes de Oliveira, D. Crist\\'obal-Hornillos, G. Mart\\'inez-Solaeche, H. V\\'azquez Rami\\'o, Ignasi P\\'erez-R\\`afols, Isabel M\\'arquez, J.A. Fern\\'andez-Ontiveros, J. Alcaniz, J. E. Rodr\\'iguez-Mart\\'in, J. M. V\\'ilchez, Jon\\'as Chaves-Montero, J. Varel, K. Taylor, L. Raul Abramo, Luis D\\'iaz-Garc\\'ia, Matthew M. Pieri, M. Moles, Nat\\'alia V. N. Rodrigues, N. Benitez, R.A. Dupke, R. Garc\\'ia-Benito, R. M. Gonz\\'alez Delgado, Sean S. Morrison, Silvia Bonoli, V. Marra","submitted_at":"2023-03-22T16:31:02Z","abstract_excerpt":"This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over $\\sim$ 1 deg$^2$ in the AEGIS field. We developed two algorithms based on artificial neural networks (ANN) to classify objects into four categories: stars, galaxies, quasars at low redshift ($z < 2.1)$, and quasars at high redshift ($z \\geq 2.1$). As inputs, we used miniJPAS fluxes for one of the classifiers (ANN$_1$) and colours for the other (ANN$_2$). The ANNs were trained and tested using mock data in the first place. We studie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12684","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/2303.12684/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"}