用自监督方法提升星光曲线嵌入效果，适合少样本天体分类。
ASTROCO: Self-Supervised Conformer-Style Transformers for Light-Curve Embeddings
- 结合注意力与深度卷积，捕捉光变曲线的全局与局部特征。
- 在MACHO R波段上误差比Astromer降低61%~70%，宏平均F1提升7%。
- 生成的嵌入可高效迁移至小样本分类任务，适合标注稀缺场景。
我们提出AstroCo，一种适用于不规则恒星光变曲线的Conformer风格编码器。通过融合注意力机制、深度卷积和门控结构，AstroCo能够同时捕捉光变曲线中的全局依赖关系与局部细节特征。在MACHO R波段数据集上，AstroCo相比Astromer v1和v2分别实现70%和61%的误差降低，宏观F1值相对提升约7%，且生成的嵌入具有良好的跨任务迁移能力，适用于少样本分类任务。这些结果表明，AstroCo具备成为时域天文学中强大且标签高效的预训练基础模型的潜力。
原文摘要 · Abstract (English)
We present AstroCo, a Conformer-style encoder for irregular stellar light curves. By combining attention with depthwise convolutions and gating, AstroCo captures both global dependencies and local features. On MACHO R-band, AstroCo outperforms Astromer v1 and v2, yielding 70 percent and 61 percent lower error respectively and a relative macro-F1 gain of about 7 percent, while producing embeddings that transfer effectively to few-shot classification. These results highlight AstroCo's potential as a strong and label-efficient foundation for time-domain astronomy.
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