SpecGrav -- Detection of Gravitational Waves using Deep Learning

TL;DR

SpecGrav introduces a deep learning approach using 2D CNNs and spectrograms to detect gravitational waves efficiently, significantly reducing training time and hardware requirements compared to traditional methods.

Contribution

The paper presents a novel deep learning model that detects gravitational waves from spectrograms with minimal training time and hardware resources.

Findings

Training time reduced to 19 minutes on a 2GB GPU

Effective detection of black hole and neutron star mergers

Deep learning outperforms traditional matched filtering methods

Abstract

Gravitational waves are ripples in the fabric of space-time that travel at the speed of light. The detection of gravitational waves by LIGO is a major breakthrough in the field of astronomy. Deep Learning has revolutionized many industries including health care, finance and education. Deep Learning techniques have also been explored for detection of gravitational waves to overcome the drawbacks of traditional matched filtering method. However, in several researches, the training phase of neural network is very time consuming and hardware devices with large memory are required for the task. In order to reduce the extensive amount of hardware resources and time required in training a neural network for detecting gravitational waves, we made SpecGrav. We use 2D Convolutional Neural Network and spectrograms of gravitational waves embedded in noise to detect gravitational waves from binary black hole merger and binary neutron star merger. The training phase of our neural network was of about just 19 minutes on a 2GB GPU.