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pyBumpHunter: A model independent bump hunting tool in Python for high energy physics analyses [PDF]
SciPost Physics Codebases, 2022 The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any ...
L. Vaslin +3 more
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Making digital objects FAIR in high energy physics: An implementation for Universal FeynRules Output (UFO) models [PDF]
SciPost Physics Codebases, 2022 Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation of these digital objects.
M. Neubauer, Avik Roy, Zijun Wang
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 +3 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 +19 more
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Deployment of High Energy Physics software with a standard method [PDF]
Journal of Physics: Conference Series, 2022 The installation and maintenance of scientific software for research in experimental, phenomenological, and theoretical High Energy Physics (HEP) requires a considerable amount of time and expertise.
T. Hahn, A. Verbytskyi
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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 +4 more
semanticscholar +1 more source
Lepton flavor violation beyond the MSSM [PDF]
, 2015 Most extensions of the Standard Model lepton sector predict large lepton flavor violating rates. Given the promising experimental perspectives for lepton flavor violation in the next few years, this generic expectation might offer a powerful indirect ...
Vicente, Avelino
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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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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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Evaluating query languages and systems for high-energy physics data [PDF]
Proceedings of the VLDB Endowment, 2021 In the domain of high-energy physics (HEP), general-purpose query languages have found little adoption in analysis. This is surprising regarding SQL-based systems, as HEP data analysis matches SQL’s processing model well: the data is fully structured and
D. Graur +5 more
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