Title: Automatic classification of time-variable X-ray sources Show Chinese title

Title: 时变X射线源的自动分类

Abstract: To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the second \textit{XMM-Newton} serendipitous source catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10-fold cross validation accuracy of the training data is ${\sim}$97% on a seven-class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which, 2XMM J180658.7$-$500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ULX but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys. Show Chinese abstract

Abstract: 为了最大化未来巡天调查的发现潜力，特别是在暂现源科学领域，有必要使用自动分类来识别一些天体源。监督学习的数据挖掘技术适合解决此类问题。 在这里，我们提出了一种监督学习方法，用于自动分类第二版\textit{XMM-Newton}偶然源目录（2XMMi-DR2）中的可变X射线源。随机森林是我们选择的分类器，因为它是最准确的学习算法之一。我们的训练集由873个可变源组成，这些源的特征来源于时间序列、光谱以及其它多波段上下文信息。在七类数据集上，训练数据的10折交叉验证准确率为${\sim}$97%。我们将经过训练的分类模型应用于411个未知的2XMM可变源，生成了一个概率分类目录。利用分类边界和随机森林推导出的异常值测量方法，我们识别出12个异常源，其中2XMM J180658.7$-$500250似乎是样本中最不寻常的源。其X射线光谱表明它可能是一个ULX（超亮X射线源），但它的变化性使其非常不寻常。机器学习的分类和异常检测将在全天空巡天的时代促进科学发现。