6 papers
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…
Cross-Geometry Transfer Learning in Fast Electromagnetic Shower Simulation
Frank Gaede, Gregor Kasieczka, Lorenzo Valente
Accurate particle shower simulation remains a critical computational bottleneck for high-energy physics. Traditional Monte Carlo methods, such as Geant4, are computationally prohib…
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…