{"paper":{"title":"Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"A. Ghosh, C. Sif\\'on, G. Aguilar-Arg\\\"uello, G. Fuentes-Pineda, G. Martin, H. M. Hern\\'andez-Toledo, J. A. V\\'azquez-Mata, L. A. Mart\\'inez-V\\'azquez, R. Demarco, S. Brough, W. J. Pearson, Y. Jim\\'enez-Teja","submitted_at":"2025-01-10T20:50:19Z","abstract_excerpt":"We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric ($C,\\,A,\\,S,\\,A_S,\\,\\mathrm{Gini},\\,M_{20},\\,c_{5090}$), parametric (S\\'ersic index, $n$), geometric (axial ratio, $BA$), global colour ($g-i,\\,u-r,\\,u-i$), colour gradient ($\\Delta (g - i)$), and asymmetry gradient ($\\Delta A_{9050}$) information, all estimated for a local galaxy sample ($z<0.15$) compiled from the Sloan Digital Sky Survey (SDSS) imaging data. We trai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06340","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/2501.06340/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"}