Searching for outliers in the Chandra Source Catalog

TL;DR

This paper employs PCA and an unsupervised random forest algorithm to identify outliers in the Chandra Source Catalog, revealing interesting sources that warrant further investigation.

Contribution

It introduces a novel outlier identification algorithm applied to X-ray sources, highlighting previously known and new interesting targets.

Findings

119 high-significance outliers identified consistently

Outliers include sources with unusual features previously documented

Method effectively highlights sources for further detailed study

Abstract

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.