AI 中文总结
本综述探讨人工智能在宜居带系外行星凌星信号搜索各环节的进展，指出其可提升分析效率、恢复弱信号，结合物理建模等能提高发现效率与可靠性。
AI 中文摘要
搜寻恒星宜居带中的类地行星，本质上是在带有噪声的光变曲线中寻找数量极少、深度极浅且间隔极宽的凌星信号。在类太阳恒星周围，宜居带行星通常具有较长的轨道周期，因此有限的观测基线可能仅能记录到少量凌星事件。与此同时，恒星自身的变异性、仪器系统误差以及数据缺口会掩盖这些微弱信号，或产生类似凌星的亮度变化。本综述探讨了人工智能辅助光变曲线预处理、弱信号搜索、候选天体验证以及参数推断方面的最新进展，并考量了预训练模型与多模态模型的潜在作用。综合来看，所综述的研究表明，人工智能在提升大规模光变曲线分析效率、在部分注入测试中于低信噪比条件下恢复更多信号、减少需要人工审查的目标数量方面具有实际应用价值。本综述得出结论，将人工智能与物理建模、统计推断及后续观测相结合，为提升宜居带候选天体研究的发现效率与可靠性提供了可行路径。
英文摘要
Searching for Earth-sized planets in stellar habitable zones ultimately means finding a small number of very shallow, widely separated transits in noisy light curves. Around Sun-like stars, habitable-zone planets generally have long orbital periods, so a finite observing baseline may record only a few transits. At the same time, intrinsic stellar variability, instrumental systematics, and data gaps can obscure these weak events or produce transit-like brightness changes. This review examines recent progress in AI-assisted light-curve preprocessing, weak-signal searches, candidate vetting, and parameter inference, and considers the potential roles of pretraining and multimodal models. Taken together, the studies reviewed here show that AI has practical value in making large-scale light-curve analysis more efficient, recovering more signals at low signal-to-noise ratios in some injection tests, and reducing the number of targets requiring manual review. This review concludes that combining AI with physical modeling, statistical inference, and follow-up observations provides a practical route to improve both the discovery efficiency and the reliability of habitable-zone candidate studies.
CommentsThis paper has been withdrawn by the authors for administrative reasons related to our institutional research policy
