A Study of Artificial Intelligence in Gravitational Wave Analysis

Gravitational wave research encompasses a variety of complex challenges that have significantly propelled advancements in the field of astrophysics and signal processing. Major research hurdles include the classification and cancellation of instrumental glitches, the denoising of gravitational wave signals, the detection of binary black hole mergers, the identification of gravitational wave bursts, and numerous secondary issues that collectively enhance our understanding of these cosmic phenomena. This paper investigates the growing application of artificial intelligence (AI), deep learning, and machine learning (ML) methodologies in addressing these critical problems. The main goal is to provide a comprehensive summary of how contemporary AI techniques and deep learning techniques assist in the analysis of gravitational waves. With the evolution of computational power, especially through the use of high-performance GPUs and specialized software frameworks, AI-driven techniques have become instrumental over the past decade in the detection, classification, and mitigation of noise within gravitational wave data. This work offers a comprehensive evaluation of the adoption trends of these advanced methods, including an analysis of the computational tools employed, their performance capabilities, and inherent limitations. Additionally, it highlights the transformative role of AI in enhancing data analysis efficiency and accuracy in the realm of gravitational wave astronomy.