Results 21 to 30 of about 41,775 (139)
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 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 +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 +3 more
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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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Finetuning foundation models for joint analysis optimization in High Energy Physics [PDF]
Machine Learning: Science and TechnologyIn 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
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Leveraging State-of-the-Art Engines for Large-Scale Data Analysis in High Energy Physics
Journal of Grid Computing, 2023 The Large Hadron Collider (LHC) at CERN has generated a vast amount of information from physics events, reaching peaks of TB of data per day which are then sent to large storage facilities.
V. Padulano +4 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 +5 more
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HEPnOS: a Specialized Data Service for High Energy Physics Analysis
IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, 2023 In this paper, we present HEPnOS, a distributed data service for managing data produced by high-energy physics (HEP) experiments. Using HEPnOS, HEP applications can use HPC resources more efficiently than traditional file-based applications.
Sajid Ali
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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
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Software Sustainability & High Energy Physics [PDF]
arXiv.org, 2020 New facilities of the 2020s, such as the High Luminosity Large Hadron Collider (HL-LHC), will be relevant through at least the 2030s. This means that their software efforts and those that are used to analyze their data need to consider sustainability to ...
D. Katz +18 more
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