Results 21 to 30 of about 3,929,652 (265)
Uncertainty-aware machine learning for high energy physics [PDF]
Physical Review D, 2021 Machine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in high-dimensional feature
A. Ghosh, B. Nachman, D. Whiteson
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Quantum Algorithm for High Energy Physics Simulations.
Physical Review Letters, 2021 Simulating quantum field theories is a flagship application of quantum computing. However, calculating experimentally relevant high energy scattering amplitudes entirely on a quantum computer is prohibitively difficult.
B. Nachman +3 more
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Quantum machine learning in high energy physics [PDF]
Machine Learning: Science and Technology, 2020 Machine learning has been used in high energy physics (HEP) for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable ...
W. Guan +6 more
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Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics [PDF]
Journal of Instrumentation, 2021 We present a novel implementation of classification using the machine learning/artificial intelligence method called boosted decision trees (BDT) on field programmable gate arrays (FPGA). The firmware implementation of binary classification requiring 100
Tae Min Hong +6 more
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Loop-free tensor networks for high-energy physics [PDF]
Philosophical Transactions of the Royal Society A, 2021 This brief review introduces the reader to tensor network methods, a powerful theoretical and numerical paradigm spawning from condensed matter physics and quantum information science and increasingly exploited in different fields of research, from ...
S. Montangero, E. Rico, P. Silvi
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High Energy Physics, 1991 An intense analysis effort on the data we obtained in a seven month run on E704 last year has produced a flood of new results on polarization effects in particle production at 200 GeV/c. We are fortunate to be able to report in detail on those results. Our other Fermilab experiment, E683 (photoproduction of jets) has been delayed an unbelievable amount
K. K. Phua, Y. Yamaguchi
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, 1992 This report discusses high energy physics research being conducted at the Stanford Linear Collider using the time projection chamber and the SLD detector.
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Searching for exotic particles in high-energy physics with deep learning [PDF]
Nature Communications, 2014 Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine-learning approaches ...
P. Baldi, Peter Sadowski, D. Whiteson
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Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits [PDF]
Journal of Physics G: Nuclear and Particle Physics, 2020 One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds.
S. Wu +14 more
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Masked particle modeling on sets: towards self-supervised high energy physics foundation models [PDF]
Machine Learning: Science and TechnologyWe propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data.
T. Golling +6 more
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