60 citations · 73 across the 7 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…
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…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Erik Buhmann, Frank Gaede, Gregor Kasieczka +4
Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) m…
CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Erik Buhmann, Sascha Diefenbacher, Engin Eren +6
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with ge…
New Angles on Fast Calorimeter Shower Simulation
Sascha Diefenbacher, Engin Eren, Frank Gaede +5
The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning…
Hadrons, Better, Faster, Stronger
Erik Buhmann, Sascha Diefenbacher, Engin Eren +7
Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-lea…