Optimized Detection of Continuous Gravitational-Wave Signals using Convolutional Neural Network

Identifying gravitational waves produced by binary black hole mergers has sparked an unheard-of revolution in physics and astronomy. However, due to the low magnitudes of gravitational wave signals and the inevitability of detector noise, proper processing of gravitational wave data is in high demand. In this research, we propose a deep learning technique termed convolution neural network based on a set of time-series data from “www.kaggle.com/datasets/andradaolteanu/g2net-gravitational-wave-dataset” that contains simulated gravitational waves with noise. This work uses a convolution neural network and Fourier transformation-based gravitational wave signal recognition model (CNN). According to experimental findings, the frequency-domain signal graph describes the gravitational wave signal’s features more accurately than the time-domain signal. The signal recognition accuracy has been improved using CNN’s convolutional processing.