Generative Machine Learning - Towards a paradigm shift in physics research?

by Ramon Winterhalder (UCLouvain)

Tuesday 16 Jan 2024, 14:00 → 16:00 Europe/Zurich

4/3-006 - TH Conference Room (CERN)

High-precision simulations based on first principles are a cornerstone of any modern physics research. For instance, as we approach the HL-LHC era, there is an ever-increasing demand for both accuracy and speed in simulations. Additionally, evidence for any new physics beyond the Standard Model remains elusive in particle experiments. Consequently, there is an urgent need to enhance the sensitivity and maximize information extraction. Modern Machine Learning (ML) techniques are emerging as a beacon of hope, potentially diminishing the limitations of current methodologies and opening doors to uncharted territory in the parameter space. In this presentation, I will first explain the basic principles of machine learning and neural networks. In particular, I will introduce normalizing flows, representing a powerful class of generative models for both fast simulations and inference. Finally, I will showcase the performance of these models compared to standard techniques in the context of LHC event generation and highlight other potential applications.

ml4hep_rw.pdf