Results 11 to 20 of about 5,077 (103)

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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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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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
Allison Reinsvold Hall, N. Skidmore, G. Benelli, B. Carlson, C. David, Jonathan Davies, Wouter 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, Michel Hernández Villanueva   +18 more
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Blaze: A High performance Big Data Computing System for High Energy Physics

Journal of Physics: Conference Series, 2023
High energy physics (HEP) is moving towards extremely high statistical experiments and super-large-scale simulation of theory. In order to handle the challenge of rapid growth of data volumes, distributed computing and storage frameworks in Big Data area
Libin Xia, Wei Sun, Xiaoyu Liu, Gongxing Sun, Xiaowei Jiang   +4 more
semanticscholar   +1 more source

Quantum data learning for quantum simulations in high-energy physics [PDF]

Physical Review Research, 2023
Quantum machine learning with parametrised quantum circuits has attracted significant attention over the past years as an early application for the era of noisy quantum processors.
Lento Nagano, Alexander Miessen, Tamiya Onodera, I. Tavernelli, F. Tacchino, K. Terashi   +5 more
semanticscholar   +1 more source

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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Evaluating generative models in high energy physics [PDF]

Physical Review D, 2022
There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP).
R. Kansal, Anni Li, Javier Mauricio Duarte, N. Chernyavskaya, M. Pierini, B. Orzari, T. Tomei   +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
semanticscholar   +1 more source

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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Leveraging an open source serverless framework for high energy physics computing

Journal of Supercomputing, 2023
CERN (Centre Europeen pour la Recherce Nucleaire) is the largest research centre for high energy physics (HEP). It offers unique computational challenges as a result of the large amount of data generated by the large hadron collider.
V. Padulano, Pablo Oliver Cortés, Pedro Alonso-Jordá, Enric Tejedor Saavedra, Sebastián Risco, Germán Moltó   +5 more
semanticscholar   +1 more source