154 citations · 392 across the 9 of their papers we have counts for
15 papers
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…
Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning
S. Lai, J. Utehs, A. Wilhahn +48
To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to se…
Software Compensation for Highly Granular Calorimeters using Machine Learning
S. Lai, J. Utehs, A. Wilhahn +61
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event in…
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
Joschka Birk, Erik Buhmann, Cedric Ewen +2
We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizi…
Full Phase Space Resonant Anomaly Detection
Erik Buhmann, Cedric Ewen, Gregor Kasieczka +3
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strateg…
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…
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…
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…
Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20300 GeV/c
B. Acar, G. Adamov, C. Adloff +454
The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimete…
Design, Construction and Commissioning of a Technological Prototype of a Highly Granular SiPM-on-tile Scintillator-Steel Hadronic Calorimeter
CALICE Collaboration
The CALICE collaboration is developing highly granular electromagnetic and hadronic calorimeters for detectors at future energy frontier electron-positron colliders. After successf…
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…
Shared Data and Algorithms for Deep Learning in Fundamental Physics
Lisa Benato, Erik Buhmann, Martin Erdmann +10
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle phys…
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…
Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
Erik Buhmann, Sascha Diefenbacher, Engin Eren +4
Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calori…