Training and onboarding initiatives in high energy physics experiments. [PDF]
Front Big DataIn this article we document the current analysis software training and onboarding activities in several High Energy Physics (HEP) experiments: ATLAS, CMS, LHCb, Belle II and DUNE. Fast and efficient onboarding of new collaboration members is increasingly important for HEP experiments.
Reinsvold Hall A +18 more
europepmc +8 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
Advanced Engineering Materials, EarlyView., 2023 A novel method for tracking structural changes in gels using widely accessible microcomputed tomography is presented and validated for various hydro‐, alco‐, and aerogels. The core idea of the method is to track positions of micrometer‐sized tracer particles entrapped in the gel and relate them to the density of the gel network.
Anja Hajnal +3 more
wiley +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