Speaker
Raghav Kansal
(Univ. of California San Diego (US))
Description
Fast, accurate detector simulations are necessary to keep up with the data collected in the coming years in HEP. Due to their stochastic nature, ML-based generative models are natural opportunities for fast, differentiable simulations. We present two such graph- and attention-based models for generating LHC-like data using sparse and efficient point cloud representations, with state-of-the-art results. We measure a three-orders-of-magnitude improvement in latency compared to LHC full simulations, and also discuss recent work on evaluation metrics for validating such ML-based fast simulations.
Authors
Javier Mauricio Duarte
(Univ. of California San Diego (US))
Raghav Kansal
(Univ. of California San Diego (US))
