Quantum Convolutional Neural Networks for High Energy Physics Data Analysis [PDF]
Physical Review Research, 2020 This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment.
Samuel Yen-Chi Chen +4 more
semanticscholar +1 more source
Quantum-inspired machine learning on high-energy physics data [PDF]
npj Quantum Information, 2020 Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems.
Timo Felser +6 more
semanticscholar +1 more source
Probing New Gauge Forces with a High-Energy Muon Beam Dump. [PDF]
Physical Review Letters, 2022 We propose a new beam-dump experiment at a future TeV-scale muon collider. A beam dump would be an economical and effective way to increase the discovery potential of the collider complex in a complementary regime.
C. Cesarotti +3 more
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Low-energy reactor neutrino physics with the CONNIE experiment [PDF]
Journal of Physics, Conference Series, 2021 The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) uses fully depleted high-resistivity CCDs (charge coupled devices) with the aim of detecting the coherent elastic scattering of reactor antineutrinos off silicon nuclei and probing physics ...
I. Nasteva
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Silicon microchannel frames for high-energy physics experiments
Journal of Instrumentation, 2021 The design of detectors used for experiments in high-energy physics requires a light, stiff, and efficient cooling system with a low material budget. The use of silicon microchannel cooling plates has gained considerable interest in the last decade.
W. Poonsawat +9 more
semanticscholar +1 more source
Accurate spectra for high energy ions by advanced time-of-flight diamond-detector schemes in experiments with high energy and intensity lasers [PDF]
Scientific Reports, 2020 Time-Of-Flight (TOF) methods are very effective to detect particles accelerated in laser-plasma interactions, but they show significant limitations when used in experiments with high energy and intensity lasers, where both high-energy ions and remarkable
M. Salvadori +32 more
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Class imbalance techniques for high energy physics [PDF]
SciPost Physics, 2019 A common problem in a high energy physics experiment is extracting a signal from a much larger background. Posed as a classification task, there is said to be an imbalance in the number of samples belonging to the signal class versus the number of ...
C. Murphy
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Application of a Convolutional Neural Network for image classification to the analysis of collisions in High Energy Physics [PDF]
EPJ Web of Conferences, 2017 The application of deep learning techniques using convolutional neural networks for the classification of particle collisions in High Energy Physics is explored.
C. F. Madrazo +3 more
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General-Purpose Parallel Computing in a High-Energy Physics Experiment at CERN
International Conference on High-Performance Computing and Networking, 1996 The CERN experiment NA48 is actively using a 64-processor Meiko CS-2 machine provided by the ESPRIT project GP-MIMD2, running, as part of their day-to-day work, simulation and analysis programs parallelized in the framework of the project.
J. Apostolakis +8 more
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Exploration at the high-energy frontier: ATLAS Run 2 searches investigating the exotic jungle beyond the Standard Model [PDF]
Physics reportsThis report presents a comprehensive collection of searches for new physics performed by the ATLAS Collaboration during the Run 2 period of data taking at the Large Hadron Collider, from 2015 to 2018, corresponding to about 140 fb$^{-1}$ of $\sqrt{s}=13$
Atlas Collaboration
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