I present EXOVEIL, a transit detection system that learns what a star's brightness should look like and flags when reality disagrees. Unlike existing systems that require phase-folded input, EXOVEIL operates on raw flux time series and can detect planets that transit only once.A Transformer world model, trained on 16,499 Kepler light curves with transit-masked self-supervised learning, predicts expected stellar flux. A matched-filter detector with variance weighting extracts transit signals from the prediction residuals. A learned classifier (XGBoost) separates planets from false positives, achieving AUC 0.938 on Kepler DR25. Applied to single-transit injection-recovery, EXOVEIL recovers 32% of transits at 1000 ppm depth a task where all classification-based systems score 0% by construction. A blind search of 3,737 Kepler stars yields 179 new transit-like signals not present in the DR25 TCE catalogue, including 46 monotransit candidates. Applied withoutretraining to 47 confirmed TESS planets in the PLATO LOPS2 field, EXOVEIL achieves 100% recovery, demonstrating zero-shot cross-mission transfer. At PLATO's 25-second cadence, detection reaches 100 ppm -- approaching the Earth-analog regime. I provide the first application of conformal prediction to transit detection (95.9% empirical coverage) and release the system as pip install exoveil with pretrained weights and a candidate catalogue.

翻译：本文提出EXOVEIL凌星探测系统，该系统通过学习恒星亮度理论模型，在观测数据偏离预期时自动标记异常。与依赖相位折叠输入的传统系统不同，EXOVEIL可直接处理原始光变时序数据，甚至能探测仅发生一次凌星的行星。该系统采用Transformer世界模型，通过16,499条开普勒卫星光变曲线进行凌星掩码自监督学习训练，预测恒星理论通量。基于方差加权的匹配滤波检测器从预测残差中提取凌星信号，并由XGBoost分类器区分行星信号与假阳性，在开普勒DR25数据集上达到0.938的AUC值。在单次凌星注入恢复实验中，EXOVEIL在1000 ppm深度条件下成功恢复32%的凌星事件——而所有基于分类的现有系统因架构限制此项指标均为0%。对3,737颗开普勒恒星进行盲搜，发现DR25 TCE星表中未收录的179个新型凌星候选信号，包含46个单次凌星候选体。在未经重新训练的条件下，EXOVEIL对PLATO LOPS2天区的47颗已确认TESS行星实现100%恢复，展现出零样本跨任务迁移能力。在PLATO卫星25秒采样率下，探测灵敏度可达100 ppm——逼近地球模拟信号检测阈值。本研究首次将保形预测方法应用于凌星探测（经验覆盖率达95.9%），并开源完整系统（pip install exoveil），附带预训练权重与候选星表。