用AI模型自动识别海量恒星光变数据中的行星信号
ExoMiner++ 2.0: Vetting TESS Full-Frame Image Transit Signals
- 将原用于短周期数据的智能筛选模型适配至长周期全帧图像数据
- 在多组观测条件下实现对行星信号与假阳性事件的精准分类
- 为未来系外行星普查和后续观测提供可靠候选名单
凌日系外行星巡天卫星（TESS）的全帧图像（FFI）为数百万颗恒星提供了光度时间序列，使超越预选2分钟目标的凌日搜寻成为可能。然而，FFI在凌日信号识别与甄别方面带来新挑战。本文将原本针对TESS 2分钟数据开发的ExoMiner++框架改进为ExoMiner++ 2.0，应用于FFI光变曲线。模型对本研究覆盖各扇区的阈值穿越事件进行大规模行星与非行星分类，构建统一的甄别目录，并评估不同观测条件下的性能表现。结果表明，ExoMiner++ 2.0能有效泛化至FFI领域，在长采样周期的局限下仍可稳健区分行星信号、天体物理假阳性和仪器伪迹。该工作扩展了ExoMiner++在完整TESS数据集的应用，支持未来系外行星群体研究与后续观测优先级设定。
原文摘要 · Abstract (English)
The Transiting Exoplanet Survey Satellite (TESS) Full-Frame Images (FFIs) provide photometric time series for millions of stars, enabling transit searches beyond the limited set of pre-selected 2-minute targets. However, FFIs present additional challenges for transit identification and vetting. In this work, we apply ExoMiner++ 2.0, an adaptation of the ExoMiner++ framework originally developed for TESS 2-minute data, to FFI light curves. The model is used to perform large-scale planet versus non-planet classification of Threshold Crossing Events across the sectors analyzed in this study. We construct a uniform vetting catalog of all evaluated signals and assess model performance under different observing conditions. We find that ExoMiner++ 2.0 generalizes effectively to the FFI domain, providing robust discrimination between planetary signals, astrophysical false positives, and instrumental artifacts despite the limitations inherent to longer cadence data. This work extends the applicability of ExoMiner++ to the full TESS dataset and supports future population studies and follow-up prioritization.
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