Results 21 to 30 of about 338,112 (202)

Chromo: An event generator frontend for particle and astroparticle physics [PDF]

Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023), 2023
H.-P. Dembinski, Anatoli Fedynitch, Anton Prosekin   +2 more
openalex   +2 more sources

Particle and nuclear physics parameters—how do they affect the tracks of double beta events in a germanium detector, and their separation from gamma events

Physics Letters B, 2005
Abstract The sizes of tracks of events of neutrinoless double beta decay in a germanium detector depend on particle physics and nuclear physics parameters such as neutrino mass, right-handed current parameters, etc., and nuclear matrix elements. The knowledge of this dependence is of importance, since the key to probe the existence of 0 ν β β
H. V. Klapdor‐Kleingrothaus, I. V. Krivosheina, I. V. Titkova   +2 more
openalex   +3 more sources

Simulation of solar energetic particle events with a data-driven physics-based transport model [PDF]

Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023), 2023
Ming Zhang, Lei Cheng
openalex   +2 more sources

Coprocessor integration for real-time event processing in particle physics detectors

, 2016
High-energy physics experiments today have higher energies, more accurate sensors, and more flexible means of data collection than ever before. Their rapid progress requires ever more computational power; and massively parallel hardware, such as graphics cards, holds the promise to provide this power at a much lower cost than traditional CPUs.
Alexey Pavlovich Badalov
openalex   +2 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

Physical characterization of aerosol particles during nucleation events [PDF]

Tellus B: Chemical and Physical Meteorology, 2001
Particle concentrations and size distributions have been measured from different heights inside and above a boreal forest during three BIOFOR campaigns (14 April–22 May 1998, 27 July–21 August 1998 and 20 March–24 April 1999) in Hyytiälä, Finland. Typically, the shape of the background distribution inside the forest exhibited 2 dominant modes: a fine ...
Claudia Hoell, E. D. O. Becker, M. Väkevä, J. Salm, G. Buzorius, Markku Kulmala, Hans-Christen Hansson, Pasi Aalto, Kaarle Hämeri, Rodney J. Weber, Hans Karlsson, Ismo K. Koponen, Colin D. O'Dowd, Colin D. O'Dowd, Jyrki M. Mäkelä   +14 more
openaire   +3 more sources

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

European Physical Journal C: Particles and Fields, 2021
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector ...
Joosep Pata, Javier Duarte, Jean-Roch Vlimant, Maurizio Pierini, Maria Spiropulu   +4 more
doaj   +1 more source