Deep Learning Search for Gravitational Waves from Compact Binary Coalescence

TL;DR

This paper presents a hybrid deep learning approach combining matched filtering and CNNs to efficiently detect gravitational waves from compact binary coalescences, reducing computational costs while maintaining detection accuracy.

Contribution

The study introduces a novel hybrid method that integrates matched filtering with convolutional neural networks, eliminating the need for $ ext{chi}^2$ tests and improving efficiency in gravitational wave searches.

Findings

Achieves detection efficiency comparable to standard methods.

Reduces computational resource requirements.

Handles complex signals with physical effects not in templates.

Abstract

Gravitational wave searches rely on a combination of methods, including matched filtering, coherent analyses, and more recent machine learning based pipelines. For compact binary coalescences, where signals originate from the relativistic dynamics of compact objects, matched filtering remains a central element, but its computational cost will increase substantially with the data volumes and parameter-space coverage required by next-generation interferometers such as the Einstein Telescope. Developing complementary strategies that reduce computational load while preserving detection performance is therefore essential. We investigate a hybrid approach that combines matched-filtering concepts with Convolutional Neural Networks, enabling efficient signal searches without relying on the usual $\chi^2$ rejection test. Using simulated data sets that include injected signals in Gaussian noise, transient noise, and physical effects not represented in template bank, such as eccentricity, precession and higher-order modes, we show that the method achieves a detection efficiency comparable to a standard matched-filtering search while offering a more resource efficient pipeline. These results indicate that deep learning assisted searches can support sustainable gravitational-wave data analysis in future detector eras.