I will describe a research program aimed at advancing the potential for discovery and interdisciplinary collaboration by approaching fundamental physics challenges through the lens of modern machine learning (ML). This research program has two complementary components. Ab initio simulations are a powerful tool of fundamental physics and the first component described in the talk will be the optimal combination of simulations with ML. The second component will focus on simulation-free problems where ML can be used to identify patterns in high-dimensional feature spaces that would be unfindable with traditional methods. I will give illustrative examples of physics analysis we can do now that would be unimaginable before deep learning!
Bio
Benjamin Nachman is the group leader of the cross-cutting Machine Learning for Fundamental Physics group and is a member of the ATLAS Collaboration at CERN. Nachman develops, adapts, and deploys machine learning algorithms to enhance data analysis in high energy physics. His research interests also include anomaly detection, likelihood-free inference, particle physics, and quantum computing.
Location:
373 Cory Hall