标题： 一种周期变星的新周期确定方法 显示英文标题

标题： A New Period Determination Method for Periodic Variable Stars

摘要： 变星在理解银河系和宇宙中起着关键作用。 天文学大数据时代为快速识别有趣且重要的变星带来了新的挑战。 准确估计周期是区分不同类型的变星最重要的步骤。 在此，我们提出了一种确定变光周期的新方法。 通过结合光变曲线的统计参数、变星的颜色、窗函数和GLS算法，排除了非周期性变星，并将周期性变星分为食双星和NEB变星（除食双星以外的其他类型周期性变星），推导出这两种主要类型变星的周期。 我们构建了一个随机森林分类器，基于来自ASAS-SN和OGLE变星数据集的241,154个周期性变星。 该随机森林分类器在17个特征上进行训练，其中11个是从光变曲线中提取的，6个来自Gaia早期DR3、ALLWISE和2MASS目录。 这些变星被分类为7个超级类别和17个子类别。 与ASAS-SN和OGLE目录相比，分类准确率通常高于约82%，周期准确率为70%-99%。 为了进一步测试新方法和分类器的可靠性，我们将结果与Chen等人（2020）对ZTF DR2的结果进行了比较。 分类准确率通常高于70%。 EW和SR变星的周期准确率分别为50%和53%。 而其他类型变星的周期准确率为65%-98%。 显示英文摘要

摘要： Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods is the most important step to distinguish different types of variable stars. Here, we propose a new method of determining the variability periods. By combining the statistical parameters of the light curves, the colors of the variables, the window function and the GLS algorithm, the aperiodic variables are excluded and the periodic variables are divided into eclipsing binaries and NEB variables (other types of periodic variable stars other than eclipsing binaries), the periods of the two main types of variables are derived. We construct a random forest classifier based on 241,154 periodic variables from the ASAS-SN and OGLE datasets of variables. The random forest classifier is trained on 17 features, among which 11 are extracted from the light curves and 6 are from the Gaia Early DR3, ALLWISE and 2MASS catalogs. The variables are classified into 7 superclasses and 17 subclasses. In comparison with the ASAS-SN and OGLE catalogs, the classification accuracy is generally above approximately 82% and the period accuracy is 70%-99%. To further test the reliability of the new method and classifier, we compare our results with the results of Chen et al. (2020) for ZTF DR2. The classification accuracy is generally above 70%. The period accuracy of the EW and SR variables is 50% and 53%, respectively. And the period accuracy of other types of variables is 65%-98%.