Microlensify：一种基于 Transformer 的微引力透镜事件机器学习分类器，基于凌星系外行星巡天卫星（TESS）光变曲线训练

本文介绍了一种名为 Microlensify 的机器学习模型，该模型利用 Transformer 架构在凌星系外行星巡天卫星（TESS）数据中识别微引力透镜事件。研究通过模拟数据与真实观测数据训练模型，有效筛选出候选事件并识别了多种误报来源。该方法在处理高频次巡天数据时展现了良好的分类性能与事件持续时间估算能力。

arXiv:2608.19419v1 Announce Type: new Abstract: 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