Detection of Gravitational Waves Using Machine Learning

Gravitational wave detection has become a land-mark in astrophysics, shedding light on the most dynamic processes in the universe. However, extracting important signals from noisy data is still a challenge. This review explores the use of machine learning techniques, specifically Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Random Forests, to improve the accuracy and operation of gravity detection. We perform a comparative analysis of the algorithms in the context of gravitational wave astronomy, evaluating their performance, advantages, and limitations. The results of this investigation lead to an ongoing debate about the best combination of the machine learning techniques to improve gravitational wave signal extraction.