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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, U. 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
semanticscholar   +1 more source

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, Ben 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, G. A. Stewart, J. Veatch, Michel Hernández Villanueva   +18 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
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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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, Martin Ritter, E. Rodrigues, J. Rumsevicius, L. Sexton-Kennedy, N. Smith, G. Stewart, S. Wertz   +24 more
semanticscholar   +1 more source

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, C. Leggett, Meifeng Lin, V. Pascuzzi, A. Strelchenko, V. Tsulaia, B. Viren, Tianle Wang, B. Yeo, Haiwang Yu   +19 more
semanticscholar   +1 more source

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
semanticscholar   +1 more source

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

Boosted decision trees [PDF]

Artificial Intelligence for High Energy Physics, P. Calafiura, D. Rousseau and K. Terao, eds., pp. 9-58, World Scientific Publishing, 2022, 2022
Boosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the high-energy physics context and describing ways to quantify the performance and training quality of classifiers, decision trees are described.
arxiv   +1 more source