Detection of Gravitational Wave Signals from Precessing Binary Black Hole Systems using Convolutional Neural Network

TL;DR

This paper develops a convolutional neural network approach to detect gravitational wave signals from binary black hole systems, including precessing spins, achieving high accuracy and offering a computationally efficient alternative to traditional template-based methods.

Contribution

It introduces a hierarchical CNN-based classification method for identifying GW signals from precessing and aligned binary black holes, improving detection accuracy and efficiency.

Findings

Achieves over 99% accuracy in noise vs. GW signal classification.

Classifies precessing vs. aligned systems with around 95% accuracy.

Successfully applied to real LIGO data to identify BBH events.

Abstract

Current searches for gravitational waves (GWs) from black hole binaries using the LIGO and Virgo observatories are limited to analytical models for systems with black hole spins aligned (or anti-aligned) with the orbital angular momentum of the binary. Detecting black hole binaries with precessing spinsis crucial for gaining unique astrophysical insights into the formation of these sources. Therefore, it is essential to develop a search strategy capable of identifying compact binaries with precessing spins. Aligned-spin waveform models are inadequate for detecting compact binaries with high precessing spins. While several efforts have been made to construct template banks for detecting precessing binaries using matched filtering, this approach requires many templates to cover the entire search parameter space, significantly increasing the computational cost. This work explores the detection of GW signals from binary black holes(BBH) with both aligned and precessing spins using a convolutional neural network (CNN). We frame the detection of GW signals from aligned or precessing BBH systems as a hierarchical binary classification problem. The first CNN model classifies strain data as either pure noise or noisy signals (GWs from BBH). A second CNN model then classifies the detected noisy signal data as originating from either precessing or non-precessing (aligned/anti-aligned) systems. Using simulated data, the trained classifier distinguishes between noise and noisy GW signals with more than 99% accuracy. The second classifier further differentiates between aligned and highly precessing signals with around 95% accuracy. We extended our analysis to a multi-detector framework by performing a coincident test. Additionally, we tested the performance of our trained architecture on data from the first three observation runs of LIGO to identify detected BBH events as either aligned or precessing.