Results 21 to 30 of about 24,203 (137)

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, S. Calvet, Vincent Barra, J. Donini   +3 more
semanticscholar   +1 more source

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

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

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
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

A Serverless Engine for High Energy Physics Distributed Analysis [PDF]

IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, 2022
The Large Hadron Collider (LHC) at CERN has generated in the last decade an unprecedented volume of data for the High-Energy Physics (HEP) field. Scientific collaborations interested in analysing such data very often require computing power beyond a ...
Jacek Kusnierz, V. Padulano, Maciej Malawski, Kamil Burkiewicz, E. T. Saavedra, Pedro Alonso-Jordá, M. Pitt, V. Avati   +7 more
semanticscholar   +1 more source

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
semanticscholar   +1 more source

Evaluating Awkward Arrays, uproot, and coffea as a query platform for High Energy Physics Data

Journal of Physics: Conference Series, 2023
Query languages for High Energy Physics (HEP) are an ever present topic within the field. A query language that can efficiently represent the nested data structures that encode the statistical and physical meaning of HEP data will help analysts by ...
L. Gray, F. B. I. N. Smith
semanticscholar   +1 more source

Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations [PDF]

arXiv.org, 2022
The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), including simulating detector ...
Vincent Dumont, X. Ju, Juliane Mueller
semanticscholar   +1 more source

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