{"paper":{"title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA","astro-ph.SR"],"primary_cat":"astro-ph.IM","authors_text":"Aaron Do, Benjamin M. Boyd, Brian Cherinka, Connor Stone, David Chemaly, Erin E. Hayes, Francois Lanusse, Helen Qu, Henry W. Leung, Ioana Ciuc\\u{a}, Jeff Shen, Jeroen Audenaert, John F. Wu, Juan Rafael Mart\\'inez-Galarza, Kaisey Mandel, Kartheik G. Iyer, Liam H. Parker, Lucas Meyer, Maja Jablonska, Marc Huertas-Company, Matthew Grayling, Micah Bowles, Michael J. Smith, Mike Walmsley, Miles Cranmer, Peter Melchior, Shirley Ho, The Multimodal Universe Collaboration. Eirini Angeloudi, Tom Hehir","submitted_at":"2024-12-03T16:21:17Z","abstract_excerpt":"We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observations, constituting 100\\,TB of multi-channel and hyper-spectral images, spectra, multivariate time series, as well as a wide variety of associated scientific measurements and \"metadata\". In addition, we include a range of benchmark tasks representative of standard practices for machine learning methods in astrophysics. This massive dataset will en"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.02527","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/2412.02527/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"}