74 citations · 285 across the 14 of their papers we have counts for
21 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…
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…
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…
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…
Agents of Discovery
Sascha Diefenbacher, Anna Hallin, Gregor Kasieczka +3
The substantial data volumes encountered in modern particle physics and other domains of fundamental physics research allow (and require) the use of increasingly complex data analy…
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…
OmniJet-: Learning point cloud calorimeter simulations using generative transformers
Joschka Birk, Frank Gaede, Anna Hallin +3
We show the first use of generative transformers for generating calorimeter showers as point clouds in a high-granularity calorimeter. Using the tokenizer and generative part of th…
Calibrating Bayesian Generative Machine Learning for Bayesiamplification
Sebastian Bieringer, Sascha Diefenbacher, Gregor Kasieczka +1
Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural netw…
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
Erik Buhmann, Cedric Ewen, Darius A. Faroughy +7
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two nov…
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…
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…
L2LFlows: Generating High-Fidelity 3D Calorimeter Images
Sascha Diefenbacher, Engin Eren, Frank Gaede +4
We explore the use of normalizing flows to emulate Monte Carlo detector simulations of photon showers in a high-granularity electromagnetic calorimeter prototype for the Internatio…
EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
Erik Buhmann, Gregor Kasieczka, Jesse Thaler
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generati…
Calomplification -- The Power of Generative Calorimeter Models
Sebastian Bieringer, Anja Butter, Sascha Diefenbacher +7
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especia…
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…
Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
Erik Buhmann, Sascha Diefenbacher, Engin Eren +4
Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulati…
DCTRGAN: Improving the Precision of Generative Models with Reweighting
Sascha Diefenbacher, Engin Eren, Gregor Kasieczka +3
Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (GANs). We introduce a…