{"ID":2894560,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.11711","arxiv_id":"2507.11711","title":"Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer","abstract":"We explore the use of Swin Transformer V2, a pre-trained vision Transformer, for photometric classification in a multi-survey setting by leveraging light curves from the Zwicky Transient Facility (ZTF) and the Asteroid Terrestrial-impact Last Alert System (ATLAS). We evaluate different strategies for integrating data from these surveys and find that a multi-survey architecture which processes them jointly achieves the best performance. These results highlight the importance of modeling survey-specific characteristics and cross-survey interactions, and provide guidance for building scalable classifiers for future time-domain astronomy.","short_abstract":"We explore the use of Swin Transformer V2, a pre-trained vision Transformer, for photometric classification in a multi-survey setting by leveraging light curves from the Zwicky Transient Facility (ZTF) and the Asteroid Terrestrial-impact Last Alert System (ATLAS). We evaluate different strategies for integrating data f...","url_abs":"https://arxiv.org/abs/2507.11711","url_pdf":"https://arxiv.org/pdf/2507.11711v1","authors":"[\"Daniel Moreno-Cartagena\",\"Guillermo Cabrera-Vives\",\"Alejandra M. Muñoz Arancibia\",\"Pavlos Protopapas\",\"Francisco Förster\",\"Márcio Catelan\",\"A. Bayo\",\"Pablo A. Estévez\",\"P. Sánchez-Sáez\",\"Franz E. Bauer\",\"M. Pavez-Herrera\",\"L. Hernández-García\",\"Gonzalo Rojas\"]","published":"2025-07-15T20:30:21Z","proceeding":"astro-ph.IM","tasks":"[\"astro-ph.IM\",\"cs.CV\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}