Optimization of model independent gravitational wave search using machine learning

TL;DR

This paper introduces a machine learning-enhanced method to improve the sensitivity of gravitational wave searches for binary black hole mergers, successfully recovering known events and increasing detection efficiency, especially for complex signals.

Contribution

The paper presents a novel ML-based optimization of the cWB search algorithm, enhancing detection sensitivity for diverse BBH signals in LIGO data.

Findings

Recovered all previously reported BBH events with higher significance.

Achieved 26% improvement in detection efficiency for stellar-mass BBH mergers.

Demonstrated increased sensitivity to precessing and eccentric BBH signals.

Abstract

The Coherent WaveBurst (cWB) search algorithm identifies generic gravitational wave (GW) signals in the LIGO-Virgo strain data. We propose a machine learning (ML) method to optimize the pipeline sensitivity to the special class of GW signals known as binary black hole (BBH) mergers. Here, we test the ML-enhanced cWB search on strain data from the first and second observing runs of Advanced LIGO and successfully recover all BBH events previously reported by cWB, with higher significance. For simulated events found with a false alarm rate less than $1\,\mathrm{yr}^{-1}$, we demonstrate the improvement in the detection efficiency of 26% for stellar-mass BBH mergers and 16% for intermediate mass black hole binary mergers. To demonstrate the robustness of the ML-enhanced search for the detection of generic BBH signals, we show that it has the increased sensitivity to the spin precessing or eccentric BBH events, even when trained on simulated quasi-circular BBH events with aligned spins.