Detection of gravitational waves using topological data analysis and convolutional neural network: An improved approach
Instrumentation and Methods for Astrophysics2019-10-21v1High Energy Astrophysical PhenomenaMachine LearningGeneral Relativity and Quantum CosmologyData Analysis, Statistics and Probability
The gravitational wave detection problem is challenging because the noise is typically overwhelming. Convolutional neural networks (CNNs) have been successfully applied, but require a large training set and the accuracy suffers significantly in the case of low SNR. We propose an improved method that employs a feature extraction step using persistent homology. The resulting method is more resilient to noise, more capable of detecting signals with varied signatures and requires less training. This is a powerful improvement as the detection problem can be computationally intense and is concerned with a relatively large class of wave signatures.
@article{arxiv.1910.08245,
title = {Detection of gravitational waves using topological data analysis and convolutional neural network: An improved approach},
author = {Christopher Bresten and Jae-Hun Jung},
journal= {arXiv preprint arXiv:1910.08245},
year = {2019}
}