{"paper":{"title":"The S-PLUS: a star/galaxy classification based on a Machine Learning approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"A. Alvarez-Candal, A. Cortesi, A. Ederoclite, A. Galarza, A. Kanaan, A. L. Chies-Santos, A. Lopes, A. Molino, C. E. Barbosa, C. L. Barbosa, C. Mendes de Oliveira, C. Queiroz, E. Telles, F. R. Herpich, H. D. Perottoni, H. S. Xavier, J. A. Hernandez-Jimenez, J. L. Melo de Azevedo, J. L. N. Castell\\'on, K. Saha, L. M. Nakazono, L. Sampedro, L. Sodr\\'e Jr., M. L. L. Dantas, M. V. Costa-Duarte, P. A. A. Lopes, P. Coelho, R. C. Thom de Souza, R. Dupke, R. Lopes de Oliveira, S. Akras, T. Ribeiro, T. S. Gon\\c{c}alves, W. Schoenell, Y. Jim\\'enez-Teja","submitted_at":"2019-09-18T18:00:01Z","abstract_excerpt":"We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21, matched to SDSS/DR13, and morphological parameters. The metric of importance is defined as the relative decrease of the initial accuracy when all correlations related to a certain feature is vanished. In general, the broad photometric bands presented higher importance when compared to narrow ones. The influence of the morphological parameters has been evalu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.08626","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/1909.08626/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"}