Results 1 to 10 of about 2,274,594 (358)
Event generators for high-energy physics experiments [PDF]
SciPost PhysicsWe provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments ...
J. M. Campbell +99 more
+13 more sources
The use of computers in high energy physics experiments [PDF]
Journal of Physics E: Scientific Instruments, 1969 An introductory description is given of the nature of high energy physics experiments and of the characteristics of the detectors employed; the main uses of computers in this field are briefly outlined. A more detailed review is made of the role of computers in film measurement and data taking methods, indicating the limitations set by present ...
David L. Lord, G Macleod
openalex +3 more sources
Compilation of current high-energy-physics experiments [PDF]
, 1980 This is the fourth edition of our compilation of current high energy physics experiments. It is a collaborative effort of the Berkeley Particle Data Group, the SLAC library, and nine participating laboratories: Argonne (ANL), Brookhaven (BNL), CERN, DESY, Fermilab (FNAL), the Institute for Nuclear Study, Tokyo (INS), KEK, Serpukhov (SERP), and SLAC ...
C.G. Wohl, R.L. Kelly, F.E. Armstrong
openalex +8 more sources
An Unfolding Method for High Energy Physics Experiments
, 2002 10 pages, Contribution to the Conference on Advanced Statistical Techniques in Particle Physics, Durham, 18-22 March ...
Volker Blobel
openalex +4 more sources
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
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