Author

F. Gaede

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Preprint Sep 2026

Point-cloud generative models for fast calorimeter simulation across particles and geometries

Detailed Geant4 simulation of calorimeter showers is the largest single computing cost of collider experiments, and the High-Luminosity LHC will need about ten times more simulated events than are currently produced. We summarise recent progress in generative point cloud fast simulation for highly granular calorimeters...

Thorsten Buss, H. Day-hall, F. Gaede et al. · 0 citations

Preprint Sep 2026

Cross-geometry transfer and model collapse in point cloud calorimeter shower generation

Particle shower simulation is a major computational cost in high-energy physics. Monte Carlo methods such as Geant4 are accurate but expensive, while most machine learning surrogates are tied to specific detector geometries and require retraining for each design change. We study cross-geometry transfer learning with Ca...

Thorsten Buss, F. Gaede, G. Kasieczka et al. · 0 citations

Preprint Aug 2026

Transferable Fast Calorimeter Shower Generation via Multi-Geometry Pre-training

Detailed Geant4 simulation of calorimeter showers dominates the computing budget of high-energy physics experiments. Deep generative surrogates reduce this cost, but they have remained tied to the detector they were trained on, so each new geometry needs a large in-domain dataset. We study whether a single point cloud...

Thorsten Buss, H. Day-hall, F. Gaede et al. · 1 citation