Results 21 to 30 of about 40,820 (136)
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
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
core +3 more sources
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
semanticscholar +1 more source
A perspective on Quantum Gravity Phenomenology [PDF]
, 2003 I give a brief overview of some Quantum-Gravity-Phenomenology research lines, focusing on studies of cosmic rays and gamma-ray bursts that concern the fate of Lorentz symmetry in quantum spacetime.
Amelino-Camelia, Giovanni
core +3 more sources
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
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
Vector unparticle enhanced black holes: exact solutions and thermodynamics [PDF]
, 2010 Tensor and scalar unparticle couplings to matter have been shown to enhance gravitational interactions and provide corrections to the Schwarzschild metric and associated black hole structure.
Mureika, J. R., Spallucci, Euro
core +3 more sources
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
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 +5 more
semanticscholar +1 more source
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
semanticscholar +1 more source