Generative transformers and how to evaluate them (+ Lorentz-equivariant networks)
Abstract
With the increase in luminosity and detector granularity, simulation will be a significant computational challenge in the HL-LHC. To tackle this, I present developments in machine learning graph- and attention-based models for generating jets at the LHC using sparse and efficient point cloud representations of our data, which offer a three-orders-of-magnitude improvement in latency compared to full (Geant4) simulation. I also present studies on metrics for validating ML-based simulations, including the novel Frechet and kernel physics distances, which are found to be highly sensitive to typical mismodelling by ML generative models, and perspectives for future work in this area.
Date
Jun 27, 2023
Location
