{"paper":{"title":"PHANGS-ML: dissecting multiphase gas and dust in nearby galaxies using machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Adam K. Leroy, Brent Groves, Dalya Baron, Daniel A. Dale, Dario Colombo, David A. Thilker, Debosmita Pathak, Eric Emsellem, Eric W. Koch, Erik Rosolowsky, Eva Schinnerer, Francesco Belfiore, Guillermo A. Blanc, J. E. G. Peek, J\\'er\\'emy Chastenet, Jessica Sutter, John F. Wu, Jos\\'e E. M\\'endez-Delgado, Justus Neumann, Karin M. Sandstrom, Kathryn Grasha, Kirsten L. Larson, M\\'ed\\'eric Boquien, M\\'elanie Chevance, Oleg V. Egorov, Ralf S. Klessen, Thomas G. Williams, Toby Brown","submitted_at":"2024-02-06T19:15:35Z","abstract_excerpt":"The PHANGS survey uses ALMA, HST, VLT, and JWST to obtain an unprecedented high-resolution view of nearby galaxies, covering millions of spatially independent regions. The high dimensionality of such a diverse multi-wavelength dataset makes it challenging to identify new trends, particularly when they connect observables from different wavelengths. Here we use unsupervised machine learning algorithms to mine this information-rich dataset to identify novel patterns. We focus on three of the PHANGS-JWST galaxies, for which we extract properties pertaining to their stellar populations; warm ioniz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.04330","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/2402.04330/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"}