X-ray Sources Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

TL;DR

This paper introduces a machine learning classifier for X-ray source classification using EP-WXT data, achieving high accuracy and enabling rapid, autonomous identification of sources in time-domain astronomy.

Contribution

The study develops and integrates a Random Forest classifier for X-ray source classification based on EP-WXT data, demonstrating high accuracy and practical application in the EP mission pipeline.

Findings

Achieved approximately 95% accuracy on simulated data.

Achieved approximately 98% accuracy on observational data.

Identified most effective features for source classification.

Abstract

X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on systematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reliable classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed Random Forest classifier, built on selected features derived from light curves, energy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classification during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.