Results 1 to 10 of about 3,848 (91)

Potential of the Julia Programming Language for High Energy Physics Computing [PDF]

Computing and Software for Big Science, 2023
Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage.
J. Eschle, Tamás Gál, Mosé Giordano, Philippe Gras, B. Hegner, L. Heinrich, Uwe Hernandez Acosta, S. Kluth, Jerry Ling, Pere Mato, M. Mikhasenko, Alexander Moreno Briceño, J. Pivarski, Konstantinos Samaras-Tsakiris, O. Schulz, G. Stewart, J. Strube, Vassil Vassilev   +17 more
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Quantum Simulation for High-Energy Physics [PDF]

PRX Quantum, 2022
It is for the first time that Quantum Simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community.
Christian W. Bauer. Zohreh Davoudi, A. Balantekin, Tanmoy Bhattacharya, M. Carena, W. A. Jong, P. Draper, A. El-Khadra, N. Gemelke, M. Hanada, D. Kharzeev, Henry Lamm, Yingying Li, Junyu Liu, M. Lukin, Y. Meurice, C. Monroe, B. Nachman, G. Pagano, J. Preskill, E. Rinaldi, A. Roggero, D. Santiago, M. Savage, I. Siddiqi, G. Siopsis, D. Zanten, N. Wiebe, Y. Yamauchi, Kubra Yeter-Aydeniz, S. Zorzetti   +29 more
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Training and onboarding initiatives in high energy physics experiments [PDF]

Frontiers Big Data, 2023
In 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
A. Hall, N. Skidmore, G. Benelli, B. Carlson, C. David, Jonathan Davies, W. Deconinck, David DeMuth, Peter Elmer, Rocky Bala Garg, S. Hageböck, K. Lieret, V. Lukashenko, Sudhir Malik, A. Morris, H. Schellman, Graeme A. Stewart, J. Veatch, M. Villanueva   +18 more
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FAIR AI models in high energy physics [PDF]

Machine Learning: Science and Technology, 2022
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery.
Javier Mauricio Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. Huerta, Daniel Diaz, P. Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, V. Kindratenko, Farouk Mokhtar, M. Neubauer, Sang Eon Park, M. Quinnan, R. Rusack, Zhizhen Zhao   +16 more
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Constraints on Future Analysis Metadata Systems in High Energy Physics [PDF]

Computing and Software for Big Science, 2022
In high energy physics (HEP), analysis metadata comes in many forms—from theoretical cross-sections, to calibration corrections, to details about file processing.
T. J. Khoo, A. Hall, N. Skidmore, S. Alderweireldt, J. Anders, C. Burr, W. Buttinger, P. David, L. Gouskos, L. Gray, S. Hageböck, A. Krasznahorkay, P. Laycock, A. Lister, Z. Marshall, A. Meyer, T. Novak, S. Rappoccio, M. Ritter, E. Rodrigues, J. Rumsevicius, L. Sexton-Kennedy, N. Smith, G. Stewart, S. Wertz   +24 more
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The Future of High Energy Physics Software and Computing [PDF]

, 2022
Software and Computing (S&C) are essential to all High Energy Physics (HEP) experiments and many theoretical studies. The size and complexity of S&C are now commensurate with that of experimental instruments, playing a critical role in experimental ...
V. Elvira, S. Gottlieb, O. Gutsche, B. Nachman, S. Bailey, W. Bhimji, P. Boyle, G. Cerati, K. Cranmer, G. Davies, R. Gardner, K. Heitmann, M. Hildreth, Will Hopkins, T. Humble, M. Savage, P. Shanahan, K. Terao, D. Whiteson, F. Wuerthwein   +19 more
semanticscholar   +1 more source

Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics [PDF]

arXiv.org, 2023
High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapidly increasing fraction of floating point computing power in leadership ...
M. Atif, Meghna Battacharya, P. Calafiura, T. Childers, M. Dewing, Zhihua Dong, O. Gutsche, S. Habib, K. Knoepfel, M. Kortelainen, K. Kwok, Charles Leggett, Meifeng Lin, V. Pascuzzi, A. Strelchenko, V. Tsulaia, B. Viren, Tianle Wang, Beomki Yeo, Haiwang Yu   +19 more
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Masked particle modeling on sets: towards self-supervised high energy physics foundation models [PDF]

Machine Learning: Science and Technology
We 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, Lukas Heinrich, M. Kagan, Samuel Klein, Matthew Leigh, Margarita Osadchy, John Andrew Raine   +6 more
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Explainability of High Energy Physics events classification using SHAP

Journal of Physics: Conference Series, 2023
Complex machine learning models have been fundamental for achieving accurate results regarding events classification in High Energy Physics (HEP). However, these complex models or black-box systems lack transparency and interpretability. In this work, we
R. Pezoa, L. Salinas, C. Torres, Federico Santa Mar´ıa   +3 more
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Finetuning foundation models for joint analysis optimization in High Energy Physics [PDF]

Machine Learning: Science and Technology
In this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequential optimization or reconstruction and analysis components.
M. Vigl, N. Hartman, L. Heinrich
semanticscholar   +1 more source