Enhancing Gravitational-Wave Science with Machine Learning

Understand

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.