The discovery of the first gravitational wave signal, GW150914, marked the dawn of gravitational wave astronomy. Since then, over 90 signals have been detected, including landmark events such as GW170817, the first observed neutron star merger. The complexity of detecting such events poses immense challenges, requiring significant advancements in detector design and data analysis techniques. In this context, machine learning has emerged as a powerful and adaptable tool. Recent advancements in complex algorithm architectures, combined with access to large datasets and enhanced computational power, enable the application of these techniques in this field. This seminar explores the potential applications of machine learning in gravitational wave research and highlights its utility in addressing the challenges of data analysis and event detection.
