Microlensify：基于Transformer的微引力透镜事件机器学习分类器，训练自TESS光变曲线

中文摘要 AI 辅助

微引力透镜能够揭示原本难以探测的暗弱致密天体种群。全天巡天项目凭借其设计，具备在全天范围内搜寻这类天体的潜力。凌日系外行星巡天卫星（TESS）的主要设计目标是探测凌日系外行星，它也提供了接近全天覆盖的高采样率数据。本研究中，我们利用TESS数据，结合传统方法与机器学习方法搜寻微引力透镜候选体，并识别高采样率巡天中的相关假阳性信号。Microlensify是一种基于物理信息、以Transformer为基础的变分自编码器，训练自模拟的单透镜微引力透镜光变曲线以及真实的TESS第1扇区数据。该模型可对事件进行分类、重构光变曲线，并估计微引力透镜事件的持续时长。将其应用于约560万条TESS光变曲线后，它在不同TESS处理流程中识别出0.036%至1.89%的天体为微引力透镜候选体。应用微引力透镜探测指标并与SIMBAD交叉匹配后，我们得到了最终的候选体列表，识别出的假阳性信号包括长周期变星、米拉变星、激变变星、红巨星和暂现源。我们还发现了由小行星过境引发的类高斯峰值，这是高采样率微引力透镜巡天中潜在的假阳性来源。该模型预测事件持续时长的R²精度达0.97。此外，该模型在不同地面微引力透镜巡天已发表的事件上进行测试，确认其中92.7%为微引力透镜事件，证明其适用于不同采样率的巡天项目。

英文摘要

Microlensing can reveal populations of faint compact objects that are otherwise difficult to detect. Depending on their design, all-sky surveys have the potential to search for these objects across the sky. The Transiting Exoplanet Survey Satellite (TESS), primarily designed to detect transiting exoplanets, also provides near all-sky coverage with high cadence. In this work, we use TESS data to search for microlensing candidates using both traditional and machine-learning methods and to identify associated false positives in high-cadence surveys. Microlensify is a physics-informed, transformer-based variational autoencoder trained on simulated single-lens microlensing light curves and real TESS Sector 12 data. The model classifies events, reconstructs light curves, and estimates microlensing event durations. Applied to $\sim 5.6$ million TESS light curves, it identified between $0.036\%$ and $1.89\%$ as microlensing candidates across different TESS pipelines. After applying microlensing detection metrics and cross-matching with SIMBAD, we obtained a final list of candidates and identified false positives including long-period variables, Mira variables, cataclysmic variables, red giants, and transients. We also found Gaussian-like peaks caused by asteroid crossings, a potential source of false positives in high-cadence microlensing surveys. The model also predicts event duration with an accuracy of $R^2 = 0.97$. The model was further tested on published events from different ground-based microlensing surveys, confirming 92.7% as microlensing, demonstrating its applicability across surveys with different cadences.

发表机构

• Institute for Advanced Studies in Basic Sciences (IASBS)（基础科学高等研究院）
• Lehigh University（利哈伊大学）
• University of Virginia（弗吉尼亚大学）
• Isfahan University of Technology（伊斯法罕理工大学）
• University of Delaware（特拉华大学）
• Harvard-Smithsonian Center for Astrophysics（哈佛-史密森天体物理中心）

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