SEZIONE DI GENOVA
(S.Marzani, M.Raveri) Prof. Tilman Plehn (Heidelberg University) LHC as one of the most data-intensive scientific endeavors provides the perfect link between fundamental physics research and modern data science. As modern machine learning is transforming our lives, literally, no aspect of LHC physics is left untouched. This starts with identifying data for classic or optimal analyses and extends to anomaly searches and powerful simulations based on perturbative quantum field theory. I will give a few examples for the transformative power of modern machine learning in particle physics, show how our understanding of uncertainties adds new flavors to machine learning, and explain how generative neural networks allow us to realize our dream of making LHC data available to a broad scientific community.

DATA: 11-12-2024