This paper tackles the problem of effectively detecting variable celestial objects whose brightness periodically changes over time. This problem is crucial in studying the evolution and structure of the universe and elucidating physical phenomena. The method by Sesar et al. is one of the popular approaches used in detecting variable celestial objects that uses statistical data of celestial time series, such as intrinsic variability $\sigma$ and ${\chi }^2$, etc. However, since statistical data is an aggregation of celestial time series, the previous approaches do not take advantage of the periodicity, which is the inherent characteristic of variable celestial objects; it fails to find variable celestial objects effectively. To solve such a problem, we propose an approach to detecting variable celestial objects using periodic analysis. Our approach uses sparse modeling as periodic analysis since celestial time series is typically sparse and sparse modeling can effectively obtain periodicities of the celestial objects from sparse time series. By exploiting the periodicities of the celestial objects as features, we perform binary classification to estimate whether a celestial object is a variable celestial object. To show the effectiveness of our approach, we evaluated our approach using Hyper SuprimeCam (HSC) PDR2 dataset, and we confirmed that AUC of our approach is 0.939 while AUC of the previous approach is 0.750; our approach can more effectively detect variable celestial objects.

本文解决了有效检测亮度随时间周期性变化的可变天体的问题。这个问题对于研究宇宙的演化和结构以及阐明物理现象至关重要。Sesar 等人的方法。是检测可变天体的常用方法之一，它使用天体时间序列的统计数据，例如内在变异性$\sigma$和${\chi }^2$然而，由于统计数据是天体时间序列的聚合，以前的方法没有利用周期性这一可变天体的固有特征；它无法有效地发现变化的天体。为了解决这个问题，我们提出了一种使用周期分析来检测可变天体的方法。我们的方法使用稀疏建模作为周期性分析，因为天体时间序列通常是稀疏的，稀疏建模可以有效地从稀疏时间序列中获得天体的周期性。通过利用天体的周期性作为特征，我们进行二元分类来估计天体是否是可变天体。为了证明我们方法的有效性，我们使用 Hyper SuprimeCam (HSC) PDR2 数据集评估了我们的方法，并确认我们方法的 AUC 为 0.939，而之前方法的 AUC 为 0.750；我们的方法可以更有效地探测变化的天体。