Speaker
Description
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. Many previous efforts were limited to models relying on fixed regular grid-like geometries leading to artifacts when applied to highly granular calorimeters with realistic cell layouts. We present CaloClouds III, a novel point cloud diffusion model that allows for high-speed generation of realistic electromagnetic showers due to the distillation into a consistency model. The model is conditioned on incident energy and impact angles and implemented into a realistic DD4hep based simulation model of the ILD detector concept for a future Higgs factory. This is done with the DDFastShowerML library which has been developed to allow for easy integration of generative fast simulation models into any DD4hep based detector model. With this it is possible to benchmark the performance of a generative ML model using fully reconstructed physics events by comparing them against the same events simulated with Geant4, thereby ultimately judging the fitness of the model for application in an experiment’s Monte Carlo.
