Speaker
Description
Fast, reliable surrogates for detector simulation are essential to support the physics program of the high-luminosity LHC (HL-LHC) and future collider experiments. We present a generative model for pion showers in the ECal and HCal of the International Large Detector (ILD), representing each shower as a high-granularity point cloud where every point encodes position, energy, and — for the first time for hadronic showers — timing information. The model avoids the cost of direct point-cloud generation by encoding every shower into a latent representation: a diffusion process produces compact latent codes, which a VAE decoder then unfolds into full point clouds only in its final layers. Therefore, this approach is significantly cheaper than direct point-cloud generation, while still producing showers with realistic spatial and temporal structure. The latent-space formulation offers a general strategy for scaling point-cloud generative models to the high-multiplicity showers produced by highly granular calorimeters, while also incorporating timing information.