Results 21 to 30 of about 350,654 (301)
Using neural networks as an event trigger in elementary particle physics experiments [PDF]
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 2002 Elementary particle physics experiments often have to deal with high data rates. In order to avoid having to write out all data that is occurring online processors, triggers are used to cull out the uninteresting data. These triggers are based on some particular aspect of the physics being examined. At times these aspects are often equivalent to simple
E. Neis +5 more
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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
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About Waves, Particles, Events, Computer Simulation, and Ethics in Quantum Physics
, 2017 Anne Dippel, Martin Warnke
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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 +2 more
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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
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, 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
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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 +8 more
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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 +2 more
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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 +2 more
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