Enhancing Gravitational-Wave Science with Machine Learning

Just put on the arXiv, a comprehensive review of the current state of machine learning applications to various problems in gravitational-wave astrophysics. The abstract reads:

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future gravitational-wave detectors.

This work benefits from many discussions with colleagues from the LIGO and Virgo collaboration, as well as the COST action G2Net (authors from CAMK: Michał Bejger, Filip Morawski).