{"paper":{"title":"Classification of Eclipsing Binary Light Curves in Gaia DR3: A Machine Learning Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Bedri Keskin, \\\"Ozg\\\"ur Ba\\c{s}t\\\"urk","submitted_at":"2026-06-19T01:02:33Z","abstract_excerpt":"Gaia Data Release 3 (DR3) presents a unique dataset with approximately 2.1 million eclipsing binary star candidates. The unsustainability of manually classifying such a large volume of data has necessitated the development of reliable and scalable automated techniques. In this study, a novel multimodal deep learning model has been developed for the automated classification of approximately 2 million eclipsing binary stars in the Gaia DR3 archive based on their light curve morphologies (EA, EB, EW). The developed architecture simultaneously utilizes a Convolutional Neural Network (CNN) that ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21017","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/2606.21017/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"}