Autoclassification of the Variable 3XMM Sources Using the Random Forest Machine Learning Algorithm

TL;DR

This paper demonstrates the successful application of the Random Forest machine learning algorithm to automatically classify variable sources in the 3XMM catalog with high accuracy, identifying new unusual sources.

Contribution

It introduces a new automated classification method for 3XMM sources using Random Forest, achieving over 90% accuracy and discovering previously unknown sources.

Findings

Classification accuracy of ~92% for variable sources

Accuracy of ~95% for identifying spurious detections

Discovery of three new unusual X-ray sources

Abstract

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ~92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ~95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.