机器学习技术来选择Be明星候选人。在OGLE-IV盖亚南黄道极场中的应用
Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field
摘要：
统计模式识别方法以相对较低的计算成本为变星分类提供了有竞争力的解决方案。为了进行监督分类，提出了一组特征，并用于训练自动分类系统。在以往的研究中，光变曲线的幅值密度和它们的傅里叶系数相关的能量被选为特征。然而，这些特征中的一些特征对于异常值的存在并不鲁棒，并且傅立叶系数的计算对于大数据集在计算上是昂贵的。我们提出了一套新的强大的功能，使用监督分类器，以寻找新的恒星候选人在OGLE-IV盖亚南黄道极场的性能进行评估。我们计算了六种类型的变星和一组被明星候选人在文献中报道的建议设置的功能。我们使用分类树和随机森林以及K-最近邻、支持向量机和梯度提升树方法来评估这些特征的性能。我们使用10倍交叉验证和网格搜索来调整分类器。我们验证了一组OGLE-IV光变曲线上的最佳分类器的性能，并将其应用于寻找新的Be星候选人。随机森林分类器优于其他分类器。通过使用随机森林分类器和颜色标准，我们发现了50颗在盖亚南黄道极场方向的Be候选恒星，其中四颗具有与Herbig Ae/Be恒星一致的红外颜色。监督方法对于从大型数据库中获取变星的初步样本是非常有用的。像往常一样，被归类为Be星候选者的恒星必须检查它们的颜色和光谱特征。
英文摘要：
Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to train an automatic classification system. Quantities related to the magnitude density of the light curves and their Fourier coefficients have been chosen as features in previous studies. However, some of these features are not robust to the presence of outliers and the calculation of Fourier coefficients is computationally expensive for large data sets. We propose and evaluate the performance of a new robust set of features using supervised classifiers in order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic pole field. We calculated the proposed set of features on six types of variable stars and on a set of Be star candidates reported in the literature. We evaluated the performance of these features using classification trees and random forests along with K-nearest neighbours, support vector machines, and gradient boosted trees methods. We tuned the classifiers with a 10-fold cross-validation and grid search. We validated the performance of the best classifier on a set of OGLE-IV light curves and applied this to find new Be star candidates. The random forest classifier outperformed the others. By using the random forest classifier and colour criteria we found 50 Be star candidates in the direction of the Gaia south ecliptic pole field, four of which have infrared colours consistent with Herbig Ae/Be stars. Supervised methods are very useful in order to obtain preliminary samples of variable stars extracted from large databases. As usual, the stars classified as Be stars candidates must be checked for the colours and spectroscopic characteristics expected for them.
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