Using machine learning to auto-tune chi-squared tests for gravitational wave searches

Understand

The sensitivity of gravitational wave searches is reduced by the presence of non-Gaussian noise in the detector data.

• These non-Gaussianities often match well with the template waveforms used in matched filter searches, and require signal-consistency tests to distinguish them from astrophysical signals.
• However, empirically tuning these tests for maximum efficacy is time consuming and limits the complexity of these tests.
• In this work we demonstrate a framework to use machine-learning techniques to automatically tune signal-consistency tests.