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- [1, 2] and attention-based [3] 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 [3], and perspectives for future work in this area.
https://arxiv.org/abs/2211.10295
ZOOM LINK: https://uci.zoom.us/j/97281056177