Speaker
Description
To reduce the high computational demand of detector simulations in high-energy physics, various generative surrogate models have been proposed. Classically, one generative model per incident particle type is trained, requiring separate trainings and model weights. This results in increased training and human effort, as well as a larger 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 that enter the calorimeter system at a wide range of angles and incident energies, without retraining or fine-tuning. This is accomplished by conditioning the model on the incident particle type, angle, and energy. 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.
AllShowers is a point-cloud-based flow matching model with a transformer architecture. Key architectural improvements include: a custom attention mask to reduce computational costs and provide a helpful inductive bias; a shower- and layer-wise optimal transport mapping for shorter, more stable flow trajectories; and allowing the model to encode all relevant detector properties in an embedding vector per layer. With AllShowers, we take a significant step towards a general generative model for calorimeter simulations.