Hybrid algorithm combining matched filtering and convolutional neural networks for searching gravitational waves from binary black hole mergers
Phys. Rev. D 113, 062004 – Published 26 March, 2026
DOI: https://doi.org/10.1103/5x62-9ldh
Abstract
Efficient searches for gravitational waves from compact binary coalescence are crucial for gravitational-wave observations. We present a proof-of-concept for a method that utilizes a neural network taking a signal-to-noise ratio map, a stack of signal-to-noise ratio time series calculated by the matched filter, as input and predicting the presence or absence of gravitational waves in observational data. We train the neural network with a data set of gravitational-wave signals from stellar-mass black hole mergers injected into stationary Gaussian noise. We use data set 1 of the mock data challenge MLGWSC-1 to assess the ability of the proposed algorithm. The estimated sensitivity distance is 2428.10 Mpc at the false alarm rate of 1 per month. These results indicate that our algorithm achieves reasonable sensitivity with practical computational resources.
