Results 31 to 40 of about 552,537 (332)

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

Black hole evaporation beyond the Standard Model of particle physics [PDF]

Journal of High Energy Physics, 2022
The observation of an evaporating black hole would provide definitive information on the elementary particles present in nature. In particular, it could discover or exclude particles beyond those present in the standard model of particle physics.
M. Baker, A. Thamm
semanticscholar   +1 more source

Challenges for unsupervised anomaly detection in particle physics [PDF]

Journal of High Energy Physics, 2021
Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to reconstruct certain types of data
Katherine Fraser, S. Homiller, Rashmish K. Mishra, B. Ostdiek, M. Schwartz   +4 more
semanticscholar   +1 more source

MadFlow: automating Monte Carlo simulation on GPU for particle physics processes [PDF]

The European Physical Journal C, 2021
We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs).
S. Carrazza, J. Cruz-Martinez, Marco Rossi, M. Zaro   +3 more
semanticscholar   +1 more source

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

Self-consistent modeling of the energetic storm particle event of November 10, 2012 [PDF]

Astronomy & Astrophysics, 2023
It is thought that solar energetic ions associated with coronal/interplanetary shock waves are accelerated to high energies by the diffusive shock acceleration mechanism.
A. Afanasiev, R. Vainio, D. Trotta, S. Nyberg, N. T. Sheshvan, H. Hietala, N. D. D. O. Physics, Astronomy, U. Turku, Finland., The Blackett Laboratory, D. Physics, I. College, London, United Kingdom., Queen Mary University London   +15 more
semanticscholar   +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

Liquid Argon Time Projection Chambers in the studies of rare events (phenomena) in particle physics and astrophysics

, 2018
Over the past few decades, the worldwide neutrino scientific community has demonstrated a tremendous amount of interest in the use of Liquid Argon Time Projection Chambers (LAr-TPCs) as detectors for rare events (phenomena e.g. neutrinos or WIMPs interaction).
A. Bubak
openalex   +5 more sources

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

Theoretical tools for neutrino scattering: interplay between lattice QCD, EFTs, nuclear physics, phenomenology, and neutrino event generators [PDF]

Journal of Physics G: Nuclear and Particle Physics, 2022
Maximizing the discovery potential of increasingly precise neutrino experiments will require an improved theoretical understanding of neutrino-nucleus cross sections over a wide range of energies.
L. A. Ruso, A. Ankowski, S. Bacca, A. Balantekin, J. Carlson, S. Gardiner, R. González-Jiménez, R. Gupta, T. Hobbs, M. Hoferichter, J. Isaacson, N. Jachowicz, W. Jay, T. Katori, F. Kling, A. Kronfeld, S. Li, H.-W. Lin, K. Liu, A. Lovato, K. Mahn, J. Menendez, A. Meyer, J. Morfín, S. Pastore, N. Rocco, M. Athar, T. Sato, A. Schwenk, P. Shanahan, L. Strigari, M. Wagman, X. Zhang, Y. Zhao, B. Acharya, L. Andreoli, C. Andreopoulos, J. Barrow, Tanmoy Bhattacharya, V. Brdar, C. Giusti, D. Kim, Y. Li, M. Lin, P. Machado, M. Martini, K. Niewczas, P. Pandey, A. Papadopoulou, R. Plestid, M. Roda, I. Simó, J. Simone, R. Sufian, J. Tena-Vidal, Y. Tsai, J. Udías   +56 more
semanticscholar   +1 more source