用合成光变曲线训练机器学习模型，提升系外行星发现效率
Exoplanet Detection Using Machine Learning Models Trained on Synthetic Light Curves
- 用逻辑回归、K近邻和随机森林等模型分析开普勒数据
- 数据增强使召回率和精确率显著提升，不同模型准确率各异
- 适合想用轻量级方法加速系外行星搜索的研究者
目前科学家手动搜寻系外行星效率低下，自1990年代以来仅确认约5000颗。本文利用机器学习方法，基于开普勒空间望远镜的数据，测试了逻辑回归、k近邻和随机森林等经典模型在系外行星探测中的表现。初步结果显示各模型具备良好预测能力，但存在数据偏差与类别不平衡问题。通过引入数据增强技术，显著提升了模型的召回率与精确率，而准确率则因模型而异。研究表明，在系外行星搜索中，数据增强能有效改善模型泛化能力。
原文摘要 · Abstract (English)
With manual searching processes, the rate at which scientists and astronomers discover exoplanets is slow because of inefficiencies that require an extensive time of laborious inspections. In fact, as of now there have been about only 5,000 confirmed exoplanets since the late 1900s. Recently, machine learning (ML) has proven to be extremely valuable and efficient in various fields, capable of processing massive amounts of data in addition to increasing its accuracy by learning. Though ML models for discovering exoplanets owned by large corporations (e.g. NASA) exist already, they largely depend on complex algorithms and supercomputers. In an effort to reduce such complexities, in this paper, we report the results and potential benefits of various, well-known ML models in the discovery and validation of extrasolar planets. The ML models that are examined in this study include logistic regression, k-nearest neighbors, and random forest. The dataset on which the models train and predict is acquired from NASA's Kepler space telescope. The initial results show promising scores for each model. However, potential biases and dataset imbalances necessitate the use of data augmentation techniques to further ensure fairer predictions and improved generalization. This study concludes that, in the context of searching for exoplanets, data augmentation techniques significantly improve the recall and precision, while the accuracy varies for each model.
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