Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

TL;DR

This paper demonstrates that artificial neural networks can enhance the detection of gravitational-wave signals associated with short gamma-ray bursts by improving classification efficiency and increasing observational distance.

Contribution

The study introduces a neural network-based approach that outperforms traditional methods in identifying gravitational-wave signals linked to short gamma-ray bursts.

Findings

Improved data classification efficiency at fixed false alarm probability.

Increased detection distance for gravitational-wave signals.

Neural network as a complementary detection method.

Abstract

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.