Results 11 to 20 of about 1,416,187 (309)

Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers [PDF]

arXiv, 2023
The complex events observed at the NOvA long-baseline neutrino oscillation experiment contain vital information for understanding the most elusive particles in the standard model. The NOvA detectors observe interactions of neutrinos from the NuMI beam at Fermilab. Associating the particles produced in these interaction events to their source particles,
А. Н. Шмаков, Alejandro Yankelevich, J. M. Bian, Pierre Baldi   +3 more
arxiv   +3 more sources

Development of Interest in Particle Physics as an Effect of School Events in an Authentic Setting

Scientia in Educatione, 2017
The Particle Physics Masterclasses are events offered by the “Netzwerk Teilchenwelt”, a German network of particle physicists, students and teachers with the intention to make original data from CERN available for own measurements of students.
Kerstin Gedigk, Michael Kobel, Gesche Pospiech   +2 more
doaj   +7 more sources

Pre-training strategy using real particle collision data for event classification in collider physics [PDF]

arXiv, 2023
This study aims to improve the performance of event classification in collider physics by introducing a pre-training strategy. Event classification is a typical problem in collider physics, where the goal is to distinguish the signal events of interest from background events as much as possible to search for new phenomena in nature.
T. Kishimoto, M. Morinaga, M. Saito, J. Tanaka   +3 more
arxiv   +3 more sources

Physics-based Simulation of the 2013 April 11 Solar Energetic Particle Event

The Astrophysical Journal
Solar energetic particles (SEPs) can pose hazardous radiation risks to both humans and spacecraft electronics in space. Numerical modeling based on first principles offers valuable insights into the underlying physics of SEPs and provides synthetic ...
Weihao Liu, Igor V. Sokolov, Lulu Zhao, Tamas I. Gombosi, Nishtha Sachdeva, Xiaohang Chen, Gábor Tóth, David Lario, Ward B. Manchester IV, Kathryn Whitman, Christina M. S. Cohen, Alessandro Bruno, M. Leila Mays, Hazel M. Bain   +13 more
doaj   +2 more sources

New developments for ALICE MasterClasses and the new Particle Therapy MasterClass [PDF]

EPJ Web of Conferences, 2020
International MasterClasses (IMC), an outreach activity of the International Particle Physics Outreach Group (IPPOG), has been bringing cutting-edge particle physics research to schoolchildren for over 15 years now.
Foka, Panagiota, Graczykowski, Łukasz, Nowakowski, Piotr   +2 more
core   +2 more sources

Baryon production from cluster hadronization [PDF]

European Physical Journal C: Particles and Fields, 2018
We present an extension to the colour reconnection model in the Monte-Carlo event generator Herwig to account for the production of baryons and compare it to a series of observables for soft physics.
Gieseke, Stefan, Kirchgaeßer, Patrick, Plätzer, Simon   +2 more
core   +5 more sources

Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data [PDF]

EPJ Web of Conferences, 2021
Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit
Andrews Michael, Burkle Bjorn, Chaudhari Shravan, Di Croce Davide, Gleyzer Sergei, Heintz Ulrich, Narain Meenakshi, Paulini Manfred, Usai Emanuele   +8 more
doaj   +1 more source

Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics [PDF]

EPJ Web of Conferences, 2021
Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with the detector is both time consuming and computationally expensive.
Hariri Ali, Dyachkova Darya, Gleyzer Sergei   +2 more
doaj   +1 more source

VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment

Applied Sciences, 2023
A novel machine learning (ML) approach based on a recurrent neural network (RNN) for event topology identification in high energy physics (HEP) is presented.
Silvia Auricchio, Francesco Cirotto, Antonio Giannini   +2 more
doaj   +1 more source

Machine learning and LHC event generation

SciPost Physics, 2023
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation.
Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Höche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winterhalder, Jure Zupan
doaj   +1 more source