论文标题
在钱德拉源目录中搜索异常值
Searching for outliers in the Chandra Source Catalog
论文作者
论文摘要
天文学家越来越面临大量信息，很难在数据海中寻找有价值的研究目标。离群识别研究是一种可用于通过呈现较小的来源来重点调查的方法，这些来源可能很有趣，因为它们不遵循基本人群的趋势。我们将主成分分析（PCA）和无监督的随机森林算法（URF）应用于Chandra源目录V.2（CSC2）的源。我们提出了119个高显着源，这些来源出现在我们离群识别算法（OIA）的所有重复应用中。我们分析了离群源的特征，并与SIMBAD数据库进行了交叉匹配。我们的离群值包含的几种来源以前通过研究确定为具有异常或有趣的特征。该OIA导致识别有趣的目标，这些目标可以激发更详细的研究。
Astronomers are increasingly faced with a deluge of information, and finding worthwhile targets of study in the sea of data can be difficult. Outlier identification studies are a method that can be used to focus investigations by presenting a smaller set of sources that could prove interesting because they do not follow the trends of the underlying population. We apply a principal component analysis (PCA) and an unsupervised random forest algorithm (uRF) to sources from the Chandra Source Catalog v.2 (CSC2). We present 119 high-significance sources that appear in all repeated applications of our outlier identification algorithm (OIA). We analyse the characteristics of our outlier sources and cross-match them with the SIMBAD data base. Our outliers contain several sources that were previously identified as having unusual or interesting features by studies. This OIA leads to the identification of interesting targets that could motivate more detailed study.
