ExoNet用多模态深度学习从凌星光变曲线中筛选出1754个高置信度系外行星候选体。
ExoNet: Calibrated Multimodal Deep Learning for TESS Exoplanet Candidate Vetting using Phase-Folded Light Curves, Stellar Parameters, and Multi-Head Attention
- 融合相位折叠光变曲线与恒星参数，通过多头注意力机制捕捉时间特征
- 在测试集上达到0.9549的AUC和86.3%准确率，识别出52个宜居带候选体
- 适用于大规模系外行星候选体筛选，尤其适合需要高置信度结果的天体物理研究
系外行星的大规模发现已成为现代天体物理学中的关键数据科学挑战。截至2026年初，美国国家航空航天局的凌星系外行星巡天卫星（TESS）已列出超过7,800个行星候选体，但确认数量不足720个。本文提出ExoNet，一种多模态深度学习框架，通过校准的后期融合架构，联合处理相位折叠的全局与局部光变曲线视图以及恒星参数特征，结合一维卷积神经网络、8头多头注意力机制对时序特征图建模，以及带有后处理温度校准的残差融合头。模型在7,585个已标注的开普勒兴趣目标上训练，测试AUC达0.9549，准确率为86.3%。将其应用于4,720个经确认但未验证的TESS行星候选体（基于TOI-TIC交叉匹配至NASA系外行星档案），共识别出1,754个高置信信号，52个宜居带候选体，其中6个为半径小于1.6倍地球半径的类地行星。TOI-5728.01与TOI-6716.01被认定为最接近地球的未确认候选体。完整消融实验表明各模态均提升AUC。代码与目录均已开源。
原文摘要 · Abstract (English)
The discovery of exoplanets at scale has become one of the defining data science challenges in modern astrophysics. NASA's Transiting Exoplanet Survey Satellite (TESS) had catalogued over 7,800 planet candidates by early 2026, yet confirmation stands at fewer than 720. This paper introduces ExoNet, a multimodal deep learning framework that jointly processes phase-folded global and local light curve views alongside stellar parameter features through a calibrated late-fusion architecture combining 1D Convolutional Neural Networks, 8-head Multi-Head Attention over temporal feature maps, and a residual fusion head with post-hoc Temperature Scaling calibration. Trained on 7,585 labeled Kepler Objects of Interest, ExoNet achieves Test AUC = 0.9549 and 86.3% accuracy. Applied to 4,720 verified unconfirmed TESS Planet Candidates with TOI-TIC cross-identification verified against the NASA Exoplanet Archive, the model yields 1,754 high-confidence signals, 52 habitable-zone candidates, and six Earth-sized habitable-zone targets below 1.6 Earth radii. TOI-5728.01 and TOI-6716.01 emerge as the most Earth-like unconfirmed candidates. Full ablation confirms each modality improves AUC. Code and catalog are openly released.
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