Training and onboarding initiatives in high energy physics experiments. [PDF]
Front Big DataReinsvold Hall A +18 more
europepmc +2 more sources
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
semanticscholar +1 more source
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
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
semanticscholar +1 more source
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
semanticscholar +1 more source
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
semanticscholar +1 more source
Systematic Analysis of the Non-Extensive Statistical Approach in High Energy Particle Collisions - Experiment vs. Theory [PDF]
Entropy, 2017 The analysis of high-energy particle collisions is an excellent testbed for the non-extensive statistical approach. In these reactions we are far from the thermodynamical limit. In small colliding systems, such as electron-positron or nuclear collisions,
G. Bíró +4 more
semanticscholar +1 more source
Off-line computing for experimental high-energy physics [PDF]
, 1992 The needs of experimental high-energy physics for large-scale computing and data handling are explained in terms of the complexity of individual collisions and the need for high statistics to study quantum mechanical processes.
Mount, Richard P.
core +1 more source