CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation

arXiv:2309.05704v2 Announce Type: replace-cross
Abstract: Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain in physics analysis. However, the majority of previous efforts were limited to models relying on fixed, regular detector readout geometries. A major advancement is the recently introduced CaloClouds model, a geometry-independent diffusion model, which generates calorimeter showers as point clouds …

abstract analysis arxiv cs.lg energy future generative geometry hep-ex hep-ph independent machine machine learning physics physics.data-an physics.ins-det simulation speed type