Results 11 to 20 of about 208,571 (205)

How-to: write a parton-level Monte Carlo particle physics event generator [PDF]

The European Physical Journal Plus, 2020
This article provides an introduction to the principles of particle physics event generators that are based on the Monte Carlo method. Following some preliminaries, instructions on how to build a basic parton-level Monte Carlo event generator for the hard interaction are given through exercises.
Andreas Papaefstathiou
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Cryogenic scintillators for search and investigation of extremely rare events in particle physics and astrophysics [PDF]

Journal of Physical Studies, 2005
The inorganic scintillator is an important element of a new type of cryogenic phonon scintillation detectors (CPSD) developed for single particle detection. These detectors exhibiting superior energy resolution and ability to identify the type of interaction event are considered as a next generation instrumentation in the search for extremely rare ...
V.B. Mikhailik, H. Kraus, M. S. Mykhaylyk   +2 more
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Monte Carlo event generators for high energy particle physics event simulation [PDF]

, 2019
Monte Carlo Community input to European Strategy ...
A. G. Buckley, Frank Krauss, Simon Plätzer, Michael H. Seymour, Simone Alioli, Jeppe R. Andersen, Johannes Bellm, J. M. Butterworth, Mrinal Dasgupta, Claude Duhr, Stefano Frixione, Stefan Gieseke, K. Hamilton, G. G. Hesketh, Stefan Hoeche, H. Jung, Wolfgang Kilian, Leif Lönnblad, Fabio Maltoni, Michelangelo L. Mangano, S. Mrenna, Zoltán K. Nagy, Paolo Nason, Emily Laura Nurse, Thorsten Ohl, C. Oleari, Andreas Papaefstathiou, Tilman Plehn, Stefan Prestel, Emanuele Ré, Jürgen Reuter, Peter Richardson, Gavin P. Salam, Marek Schoenherr, S. Schumann, F. Siegert, Andrzej Siódmok, Malin Sjödahl, Torbjörn Sjöstrand, Peter Skands, Davison E. Soper, Grégory Soyez, B.R. Webber   +42 more
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Efficient discrete-event based particle tracking simulation for high energy physics [PDF]

Computer Physics Communications, 2020
Accepted for publication in Computer Physics ...
Lucio Santi, L. Rossi, Rodrigo Castro
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Attention to the strengths of physical interactions: Transformer and graph-based event classification for particle physics experiments

, 2022
Version 3 of this paper supersedes arXiv:2211.05143v2.
Luc Builtjes, S. Caron, P. Moskvitina, C. Nellist, Roberto Ruiz de Austri, Rob Verheyen, Zhongyi Zhang   +6 more
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End-to-end simulation of particle physics events with flow matching and generator oversampling [PDF]

Machine Learning: Science and Technology
Abstract The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed.
Francesco Vaselli, Filippo Cattafesta, P. Asenov, A. Rizzi   +3 more
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Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers

, 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
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Dark matter inverse problem: Extracting particle physics from scattering events

Physical Review D, 2012
32 pages, 14 figures; references updated; revised to match journal ...
Samuel D. McDermott, Hai-Bo Yu, Kathryn M. Zurek   +2 more
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Energy flow networks: deep sets for particle jets [PDF]

Journal of High Energy Physics, 2019
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn ...
Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler   +2 more
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Deep Learning Approaches for BSM Physics: Evaluating DNN and GNN Performance in Particle Collision Event Classification [PDF]

Acta Physica Polonica B
Detecting Beyond Standard Model (BSM) signals in high-energy particle collisions presents significant challenges due to complex data and the need to differentiate rare signal events from Standard Model (SM) backgrounds. This study investigates the efficacy of deep learning models, specifically Deep Neural Networks (DNNs) and Graph Neural Networks (GNNs)
A. Çelik
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