Particle Identification

Particle identification, crucial for analyzing data from high-energy physics experiments like those at the Large Hadron Collider, aims to accurately classify subatomic particles based on their properties. Current research heavily utilizes machine learning, particularly neural networks and generative models like normalizing flows, to improve classification accuracy, especially when dealing with incomplete or noisy data, and to handle high-dimensional datasets efficiently. These advancements enhance the precision of particle physics analyses, leading to a better understanding of fundamental interactions and improving the sensitivity of searches for new physics.