Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis And Convolutional Neural Networks

TL;DR

This paper presents a novel wavelet packet and CNN-based scheme for detecting binary neutron star gravitational waves, significantly improving detection speed and accuracy over traditional methods like matched filtering.

Contribution

The study introduces a combined wavelet packet decomposition and CNN approach that enhances detection performance and efficiency for gravitational waves from binary neutron stars.

Findings

Over 960 times faster detection than matched filtering

Effective noise discrimination using wavelet packet features

High detection accuracy with real instrument noise

Abstract

This work investigates the detection of binary neutron stars gravitational wave based on convolutional neural network (CNN). To promote the detection performance and efficiency, we proposed a scheme based on wavelet packet (WP) decomposition and CNN. The WP decomposition is a time-frequency method and can enhance the discriminant features between gravitational wave signal and noise before detection. The CNN conducts the gravitational wave detection by learning a function mapping relation from the data under being processed to the space of detection results. This function-mapping-relation style detection scheme can detection efficiency significantly. In this work, instrument effects are considered, and the noise are computed from a power spectral density (PSD) equivalent to the Advanced LIGO design sensitivity. The quantitative evaluations and comparisons with the state-of-art method matched filtering show the excellent performances for BNS gravitational wave detection. On efficiency, the current experiments show that this WP-CNN-based scheme is more than 960 times faster than the matched filtering.