Speaker
Description
Fast calorimeter shower simulation is an active field of study, with numerous models having been explored. Recently, several models have explored a point cloud representation of energy deposits, as opposed to the more common image-like voxelisation of a shower. However, direct use of the output from the detailed Geant4 simulation as an input to these machine learning models is computationally prohibitive. Creating a point cloud which features a sufficiently reduced number of points while still preserving key physics observables has not been widely explored and currently requires handcrafted detector-dependent procedures. To address this, we present the step2point library, a lightweight and configurable tool designed to preprocess electromagnetic and hadronic showers into optimally compressed point-cloud representations, while preserving physically relevant shower characteristics. The step2point workflow reduces the density of raw simulation hits by merging deposits according to tunable spatial, energy, and topological criteria. These parameters are exposed to users to accommodate the specific granularity requirements of different detector concepts, enabling a flexible balance between fidelity and computational efficiency.
We demonstrate the core functionality of the step2point library using the Open Data Detector together with a publicly available step2point dataset. Benchmark studies show that the approach significantly decreases data volume while maintaining accurate simulation-level observables. The step2point library provides a common tool for the community to accelerate fast simulation R\&D and facilitates integration of point-cloud–based shower models.