5 papers
Transferable Fast Calorimeter Shower Generation via Multi-Geometry Pre-training
Thorsten Buss, Henry Day-Hall, Frank Gaede +4
Detailed Geant4 simulation of calorimeter showers dominates the computing budget of high-energy physics experiments. Deep generative surrogates reduce this cost, but they have rema…
CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Thorsten Buss, Henry Day-Hall, Frank Gaede +7
We present CaloClouds3, a model for the fast simulation of photon showers in the barrel of a high granularity detector. This iteration demonstrates for the first time how a pointcl…
CaloHadronic: a diffusion model for the generation of hadronic showers
Thorsten Buss, Frank Gaede, Gregor Kasieczka +4
Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with…
AllShowers: One model for all calorimeter showers
Thorsten Buss, Henry Day-Hall, Frank Gaede +2
Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast machine learning surrogate models have…
A First Full Physics Benchmark for Highly Granular Calorimeter Surrogates
Thorsten Buss, Henry Day-Hall, Frank Gaede +5
The physics programs of current and future collider experiments necessitate the development of surrogate simulators for calorimeter showers. While much progress has been made in th…