The data-driven future of high-energy-density physics [PDF]
Nature, 2021 High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of ...
P. Hatfield +23 more
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Event Classification with Quantum Machine Learning in High-Energy Physics [PDF]
Computing and Software for Big Science, 2020 We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics.
K. Terashi +5 more
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Perspectives in High-Energy Physics [PDF]
, 2000 I sketch some pressing questions in several active areas of particle physics and outline the challenges they present for the design and operation of detectors.Comment: 27 pages, 12 figures, uses aipproc (included) and boxedeps.
Quigg, Chris
core +3 more sources
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
Evolutionary Computation in High Energy Physics [PDF]
, 2008 Evolutionary Computation is a branch of computer science with which, traditionally, High Energy Physics has fewer connections. Its methods were investigated in this field, mainly for data analysis tasks. These methods and studies are, however, less known
Teodorescu, Liliana
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Fractal Structures of Yang–Mills Fields and Non-Extensive Statistics: Applications to High Energy Physics [PDF]
Physics, 2020 In this work, we provide an overview of the recent investigations on the non-extensive Tsallis statistics and its applications to high energy physics and astrophysics, including physics at the Large Hadron Collider (LHC), hadron physics, and neutron ...
A. Deppman +2 more
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Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics [PDF]
The European Physical Journal C, 2020 The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task.
L. Tani +3 more
semanticscholar +1 more source
Precision Crystal Calorimeters in High Energy Physics: Past, Present and Future [PDF]
, 2006 Precision crystal calorimeters traditionally play an important role in high energy physics experiments. In the last two decades, it faces a challenge to maintain its precision in a hostile radiation environment.
Zhu, Ren-Yuan
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Physics at High Energy Photon Photon Colliders [PDF]
, 1994 I review the physics prospects for high energy photon photon colliders, emphasizing results presented at the LBL Gamma Gamma Collider Workshop. Advantages and difficulties are reported for studies of QCD, the electroweak gauge sector, supersymmetry, and ...
Abbasabadi +68 more
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GPU coprocessors as a service for deep learning inference in high energy physics [PDF]
Machine Learning: Science and Technology, 2020 In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited.
J. Krupa +15 more
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