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

TL;DR

This paper introduces a machine learning framework to automatically tune chi-squared signal-consistency tests, enhancing gravitational wave search sensitivity by effectively reducing noise influence without affecting true signals.

Contribution

The paper presents a novel machine learning-based method to automatically optimize chi-squared tests for gravitational wave data analysis, improving detection sensitivity.

Findings

Improved search sensitivity by approximately 11% for high-mass black hole signals.

Successfully trained a complex model to suppress non-Gaussian noise.

Framework adaptable to various matched-filter search algorithms.

Abstract

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. We implement a new $\chi^2$ signal-consistency test targeting the large population of noise found in searches for intermediate mass black hole binaries, training the new test using the framework set out in this paper. We find that this method effectively trains a complex model to down-weight the noise, while leaving the signal population relatively unaffected. This improves the sensitivity of the search by $\sim 11\%$ for signals with masses $> 300 M_\odot$. In the future this framework could be used to implement new tests in any of the commonly used matched-filter search algorithms, further improving the sensitivity of our searches.