74 citations · 285 across the 14 of their papers we have counts for
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Point-cloud generative models for fast calorimeter simulation across particles and geometries
Thorsten Buss, Henry Day-Hall, Frank Gaede +7
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…
Cross-geometry transfer and model collapse in point cloud calorimeter shower generation
Thorsten Buss, Frank Gaede, Gregor Kasieczka +2
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 surrogat…
Jevons' Paradox and Fast Generative Simulation for HEP: Why Realistic Benchmarking is Essential
Thorsten Buss, Henry Day-Hall, Frank Gaede +7
Simulation is a major computational expense in HEP, and calorimeter simulation in particular drives the overall energy cost of our physics analyses. Future detectors will contain m…
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…
Generative Models and Statistical Validation
Sascha Diefenbacher, Sofia Palacios Schweitzer, Gregor Kasieczka
Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work,…
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…