Dark Matter Detection Using Advanced Machine Learning Algorithms in Particle Physics Experiments

Dark matter, a non-luminous component of the universe, remains one of the most enigmatic subjects in modern physics. This paper explores the integration of advanced machine learning algorithms in detecting dark matter signatures within particle physics experiments. By leveraging supervised and unsupervised learning models on high-dimensional data from detectors, we aim to enhance the precision and reliability of candidate identification. Our findings suggest that machine learning significantly improves pattern recognition in noisy datasets, offering a transformative approach to understanding the universe's unseen mass.