QPOML: A Machine Learning Approach to Detect and Characterize Quasi-Periodic Oscillations in X-ray Binaries

TL;DR

This paper introduces novel machine learning methods to detect and analyze quasi-periodic oscillations in X-ray binary systems, utilizing data from space telescopes, and provides a new Python library for future research.

Contribution

The study presents the first machine learning models for QPO detection and characterization, along with a publicly available library to facilitate further investigations.

Findings

Successful detection of QPOs using ML models

Application to data from NICER and RXTE telescopes

Foundation for discovering energy and timing phenomena

Abstract

Astronomy is presently experiencing profound growth in the deployment of machine learning to explore large datasets. However, transient quasi-periodic oscillations (QPOs) which appear in power density spectra of many X-ray binary system observations are an intriguing phenomena heretofore not explored with machine learning. In light of this, we propose and experiment with novel methodologies for predicting the presence and properties of QPOs to make the first ever detections and characterizations of QPOs with machine learning models. We base our findings on raw energy spectra and processed features derived from energy spectra using an abundance of data from the NICER and RXTE space telescope archives for two black hole low mass X-ray binary sources, GRS 1915+105 and MAXI J1535-571. We advance these non-traditional methods as a foundation for using machine learning to discover global inter-object generalizations between - and provide unique insights about - energy and timing phenomena to assist with the ongoing challenge of unambiguously understanding the nature and origin of QPOs. Additionally, we have developed a publicly available Python machine learning library, QPOML, to enable further Machine Learning aided investigations into QPOs.