CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

arXiv:2305.04847v2 Announce Type: replace-cross
Abstract: Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in …

abstract accuracy application arxiv computing cs.lg generative geometry hep-ex hep-ph independent key machine machine learning machine learning models major particle particle physics physics physics.data-an physics.ins-det simulation simulations speed them type work