Speaker
Description
The substantial computational burden associated with the use of traditional Monte Carlo simulation for the calorimeter systems of high energy physics experiments has driven the development of numerous deep generative models for fast calorimeter simulation. Recently, several models have been proposed which move away from the common image-like representation of a shower using a regular grid to a more flexible point cloud representation of energy deposits. However, directly using the large number of energy deposits produced by detailed Geant4 simulations is computationally prohibitive. For this reason, previous work has used handcrafted, detector-dependent procedures to create point clouds with a sufficiently reduced number of points, while still preserving key physics observables. To address this challenge, we present the step2point library, a lightweight and configurable tool for preprocessing electromagnetic and hadronic showers into compact point-cloud representations, while preserving physically relevant shower characteristics.
In this contribution, we demonstrate the functionality of the step2point library for detectors including the Open Data Detector (ODD), the International Large Detector (ILD), and the CLIC-like Detector (CLD), achieving significantly reduced data volumes while maintaining accurate simulation-level physics observables. We highlight the advantages of the step2point workflow with the example of the CaloClouds3 model trained on electromagnetic showers. Finally, we present initial studies aimed at producing point clouds for hadronic showers.