Applications of machine learning in gravitational wave research with current interferometric detectors

TL;DR

Machine learning is increasingly used in gravitational wave research for detector optimization, noise reduction, and signal analysis, showing promising improvements but still facing challenges for broader adoption.

Contribution

This paper provides a comprehensive overview of recent machine learning applications in gravitational wave detection and analysis, highlighting current progress, challenges, and future perspectives.

Findings

Machine learning aids in detector calibration and environmental disturbance mitigation.

ML techniques improve signal detection efficiency and robustness.

Traditional methods still dominate in many analysis areas.

Abstract

This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days, but have reached sufficient popularity to warrant an assessment of their impact across various domains, including detector studies, noise and signal simulations, and the detection and interpretation of astrophysical signals. In detector studies, machine learning could be useful to optimize instruments like LIGO, Virgo, KAGRA, and future detectors. Algorithms could predict and help in mitigating environmental disturbances in real time, ensuring detectors operate at peak performance. Furthermore, machine-learning tools for characterizing and cleaning data after it is taken have already become crucial tools for achieving the best sensitivity of the LIGO--Virgo--KAGRA network. In data analysis, machine learning has already been applied as an alternative to traditional methods for signal detection, source localization, noise reduction, and parameter estimation. For some signal types, it can already yield improved efficiency and robustness, though in many other areas traditional methods remain dominant. As the field evolves, the role of machine learning in advancing gravitational-wave research is expected to become increasingly prominent. This report highlights recent advancements, challenges, and perspectives for the current detector generation, with a brief outlook to the next generation of gravitational-wave detectors.