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arXiv · 2507.11711

Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer

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
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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.

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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. 2025-07-15. Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer. https://arxiv.org/abs/2507.11711

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