# Detection of gravitational waves using topological data analysis and convolutional neural network: An improved approach

arXiv: https://arxiv.org/abs/1910.08245 · HTML: https://mlsanity.com/paper/1910.08245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks](https://mlsanity.com/paper/2207.11583/) (2022)

## Abstract (arXiv)

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.