Blazars are active galactic nuclei with relativistic jets pointed almost directly at Earth. Blazars are characterized by strong, apparently stochastic flux variability at virtually all observed wavelengths and timescales, from minutes to years, the physical origin of which is still poorly understood. In the high-energy gamma-ray band, the Large Area Telescope aboard the Fermi space telescope (Fermi-LAT) has conducted regular monitoring of thousands of blazars since 2008. Deep learning can help uncover structure in gamma-ray blazars' complex variability patterns that traditional methods based on parametric statistical modeling or manual feature engineering may miss. In this work, we propose using a self-supervised Transformer encoder architecture to construct an effective representation of blazar gamma-ray variability. Measurement errors, upper limits, and missing data are accommodated using learned encodings. The model predicts a set of quantiles for the flux probability distribution at each time step, an architecture naturally suited for describing data generated by a stochastic process. As a proof of concept for how the model output can be analyzed to extract scientifically relevant information, a preliminary search for weekly-timescale time-reversal asymmetry in gamma-ray blazar light curves was conducted, finding no significant evidence for asymmetry.

翻译：耀变体是一类具有相对论性喷流几乎直指地球的活动星系核。其显著特征是在从分钟到年的几乎所有观测波段和时间尺度上，均表现出强烈且看似随机的流量变异性，而这一现象的物理起源仍不明确。在高能伽马射线波段，自2008年以来，费米空间望远镜上的大面积望远镜（Fermi-LAT）已对数千个耀变体进行了持续监测。深度学习能够揭示传统基于参数统计建模或手动特征工程方法可能遗漏的伽马射线耀变体复杂变异性模式中的结构。在本研究中，我们提出采用自监督Transformer编码器架构来构建耀变体伽马射线变异性的有效表征。通过可学习的编码方法处理测量误差、上限值和缺失数据。该模型在每个时间步预测流量概率分布的一组分位数，这种架构天然适用于描述随机过程生成的数据。作为概念验证，我们基于模型输出进行了科学信息提取分析，针对耀变体伽马射线光变曲线开展了周时间尺度的时间反演不对称性初步搜索，但未发现显著的不对称性证据。