用神经网络自动识别星体是否为视差双星，准确率达99.3%。
Astrometric Binary Classification Via Artificial Neural Networks
- 基于星体自行、视差和分离距离训练神经网络分类
- 在150万样本上实现99.3%准确率与0.999的AUC
- 适合处理大规模天文数据，替代传统耗时方法
盖亚任务已观测近二十亿颗恒星并评估其视差参数，导致视差双星候选数量大幅增加。当前计算方法既耗时又难以在合理时间内完成筛选。为此，本文提出一种基于人工神经网络（ANN）的机器学习方法，自动判断恒星是否构成视差双星对。利用盖亚DR3数据，以自行、视差、角距与物理间距作为特征，在150万高置信度真实双星与光学双星样本上训练与测试模型。结果显示，该方法准确率达99.3%，精确率为0.988，召回率为0.991，AUC达0.999，表明该机器学习技术在视差双星分类中极为有效。因此，所提出的ANN可作为现有方法的有力替代方案。
原文摘要 · Abstract (English)
With nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates have risen significantly. Due to the surplus of astrometric data, the current computational methods employed to inspect these astrometric binary candidates are both computationally expensive and cannot be executed in a reasonable time frame. In light of this, a machine learning (ML) technique to automatically classify whether a set of stars belong to an astrometric binary pair via an artificial neural network (ANN) is proposed. Using data from Gaia DR3, the ANN was trained and tested on 1.5 million highly probable true and visual binaries, considering the proper motions, parallaxes, and angular and physical separations as features. The ANN achieves high classification scores, with an accuracy of 99.3%, a precision rate of 0.988, a recall rate of 0.991, and an AUC of 0.999, indicating that the utilized ML technique is a highly effective method for classifying astrometric binaries. Thus, the proposed ANN is a promising alternative to the existing methods for the classification of astrometric binaries.
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