用于周期变星光变曲线的多变量时间序列 Transformer 嵌入

本文提出了一种多波段 Transformer 架构，通过融合不同测光波段的观测数据来提升周期变星的分类性能。研究表明，联合预训练的多波段编码器在 F1 分数上显著优于传统的单波段模型。

arXiv:2506.11637v2 Announce Type: replace Abstract: Astronomical surveys produce time series data by observing stellar objects across multiple photometry bands. Foundational transformer-based models, such as Astromer, encode each time series as a sequence of embeddings to classify sources. However, such models operate independently on each band and therefore do not use information across wavelengths or filters. We extend the single-band Astromer framework by introducing a fusion layer that combines single photometry band observations into a unified sequence representation, thus enabling multiband analysis for downstream tasks, namely periodic variable star classification. The challenge in adapting the encoder for multiband data lies in coordinating information across bands observed at asynchronous times. We pre-trained various multiband neural network models on 600 000 high signal-to-noise light curves from the Massive Compact Halo Object (MACHO) survey and fine-tuned them using labeled data from the Alcock catalog (derived from MACHO) and from the Asteroid Terrestrial-impact Last Alert System survey. Our results show that both proposed multiband architectures outperform the single-band models by approximately ten percentage points in the F1-score. Jointly pre-trained multiband encoders further improve the performance compared to a collection of independently pre-trained single-band encoders, reaching an improvement of 23 percentage points in the F1-score. These