Optimization of model independent gravitational wave search using machine learning

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 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.

First joint observation by the underground gravitational-wave detector, KAGRA, with GEO600

“…we exclude events with c c < 0.55. We also employ the effective number of time-frequency resolution levels used for event detection and waveform reconstruction [86] n f . In total 14 resolution levels are used in this analysis.…”