用预训练视觉Transformer融合多巡天光变曲线，提升天文暂现源分类性能。
Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer
- 采用Swin Transformer V2联合处理ZTF与ATLAS巡天数据
- 联合建模使分类准确率优于单一巡天或分离处理
- 适合大规模时域天文数据的可扩展分类任务
我们探索使用Swin Transformer V2——一种预训练视觉Transformer——在多巡天环境下对光变曲线进行测光分类，利用来自兹维基瞬变设施（ZTF）和小行星地球撞击最后警报系统（ATLAS）的数据。通过评估不同数据整合策略，发现联合处理两个巡天数据的多巡天架构表现最佳。结果凸显了建模巡天特异性特征与跨巡天交互关系的重要性，为构建未来时域天文学中可扩展的分类器提供了指导。
原文摘要 · Abstract (English)
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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