Speaker
Description
To address the high computational demand of detector simulations in high-energy physics, various generative surrogate models have been developed. Traditionally, one generative model per incident particle type is trained, requiring separate trainings and model weights. This increases training and human effort, as well as memory footprint during inference, since multiple sets of weights must be loaded. To address this, we put forward AllShowers, a single generative surrogate capable of generating showers from 12 different particle types, supporting a wide range of incident angles and energies, without the need for retraining or fine-tuning. The model receives the incident particle type and kinematics as conditional input. We demonstrate high-fidelity generation of electrons, photons, and charged and neutral hadrons in the highly granular electromagnetic and hadronic calorimeters of the International Large Detector (ILD). In addition to unifying the generation, AllShowers surpasses the fidelity of previous state-of-the-art single-particle-type models for hadronic showers in highly granular calorimeters.