Results 11 to 20 of about 3,929,652 (265)
High Energy Physics, 1997 This proposal is for the continuation of the High Energy Physics program at the University of California at Riverside. In hadron collider physics the authors will complete their transition from experiment UA1 at CERN to the DZERO experiment at Fermilab. On experiment UA1 their effort will concentrate on data analysis at Riverside. At Fermilab they will
Z Ajduk, A K Wroblewski
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The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics [PDF]
Reports on progress in physics. Physical Society, 2021 A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this
G. Kasieczka +46 more
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New directions for surrogate models and differentiable programming for High Energy Physics detector simulation [PDF]
arXiv.org, 2022 The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
A. Adelmann +10 more
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Unsupervised quantum circuit learning in high energy physics [PDF]
Physical Review D, 2022 Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy ...
Andrea Delgado, Kathleen E. Hamilton
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neos: End-to-End-Optimised Summary Statistics for High Energy Physics [PDF]
Journal of Physics: Conference Series, 2022 The advent of deep learning has yielded powerful tools to automatically compute gradients of computations. This is because training a neural network equates to iteratively updating its parameters using gradient descent to find the minimum of a loss ...
Nathan Simpson, Lukas Heinrich
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SYMBA: symbolic computation of squared amplitudes in high energy physics with machine learning [PDF]
Machine Learning: Science and Technology, 2022 The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calculations in high-energy physics, analytical ...
Abdulhakim Alnuqaydan +2 more
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Explainable AI for High Energy Physics [PDF]
arXiv.org, 2022 Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to convey information and build the desired input-output relationship are ...
M. Neubauer, Avik Roy
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Europhysics News, 1975 As the US and the USSR have moved toward a closer relationship over the past twenty years, one very successful area of mutual cooperation has been the field of high-energy physics. Both the US and USSR have at least five accelerators operating at center-of-mass energies greater than 3.0 GeV.
Ernest Malamud, Frank Nezrick
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On the Behavior of the Effective QCD Coupling alpha_tau(s) at Low Scales [PDF]
, 2002 The hadronic decays of the τ lepton can be used to determine the effective charge ατ(m τ 2,) for a hypothetical τ lepton with a mass in the range ...
S. Brodsky +10 more
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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
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