Noble Liquid Calorimetry for FCC-ee
Instruments, 2022 Noble liquid calorimeters have been successfully used in particle physics experiments for decades. The project presented in this article is that of a new noble liquid calorimeter concept, where a novel design allows us to fulfil the stringent ...
Nicolas Morange
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Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations [PDF]
EPJ Web of Conferences, 2021 The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources.
Rehm Florian +3 more
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CaloClouds: fast geometry-independent highly-granular calorimeter simulation [PDF]
Journal of Instrumentation, 2023 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 generative machine learning models would enable them to augment traditional ...
E. Buhmann +8 more
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MetaHEP: Meta learning for fast shower simulation of high energy physics experiments
Physics Letters B, 2023 For High Energy Physics (HEP) experiments, such as the Large Hadron Collider (LHC) experiments, the calorimeter is a key detector to measure the energy of particles.
Dalila Salamani +2 more
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Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation [PDF]
Physical Review D, 2023 Simulation is crucial for all aspects of collider data analysis, but the available computing budget in the High Luminosity LHC era will be severely constrained.
O. Amram, K. Pedro
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Fast and Accurate Electromagnetic and Hadronic Showers from Generative Models [PDF]
EPJ Web of Conferences, 2021 Generative machine learning models offer a promising way to efficiently amplify classical Monte Carlo generators’ statistics for event simulation and generation in particle physics. Given the already high computational cost of simulation and the expected
Buhmann Erik +10 more
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Porting HEP Parameterized Calorimeter Simulation Code to GPUs
Frontiers in Big Data, 2021 The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs.
Zhihua Dong +6 more
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The Heavy Photon Search (HPS) Software Environment [PDF]
EPJ Web of Conferences, 2020 The Heavy Photon Search (HPS) is an experiment at the Thomas Jefferson National Accelerator Facility (JLab) designed to search for a hidden sector photon (A’) in fixed-target electro-production.
Graf Norman
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Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network [PDF]
EPJ Web of Conferences, 2021 Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing.
Buhmann Erik +6 more
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Development of Future Electromagnetic Calorimeter Technologies and Applications for the Electron-Ion Collider with GEANT4 Simulations [PDF]
EPJ Web of Conferences, 2023 The Electron-Ion Collider (EIC) is a future collider planned to be built at BNL in about a decade. It will provide physicists with high luminosity and highly polarized beams with a wide range of nuclei species at different energies, covering an extensive
Shi Zhaozhong +3 more
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