Abstract
With the increase in luminosity and detector granularity, simulation will be a significant computational challenge in the HL-LHC. To tackle this, we present developments in machine learning (ML) 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. We 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.
Date
Feb 14, 2023
Location
