Generative Models in High Energy Physics

Explore generative models in this comprehensive lecture by Elham E Khoda and Aishik Ghosh, part of the "Statistical Methods and Machine Learning in High Energy Physics" program at the International Centre for Theoretical Sciences. Delve into the application of deep machine learning techniques in high energy physics research, focusing on the analysis of large-scale data from experiments like the Large Hadron Collider. Learn about classification, identification, characterization, and estimation strategies used in particle physics searches. Gain insights into the future of data-driven high energy physics and the role of artificial intelligence in uncovering new physics phenomena. Suitable for PhD students and postdoctoral researchers in theoretical or experimental particle physics and astro-particle physics with programming experience and knowledge of event generation and data analysis frameworks.