Results 1 to 10 of about 1,453,609 (345)
Applied Sciences, 2022 In a typical laboratory nanodusty plasma, nanometer-sized solid dust particles can be generated from the polymerization of reactive plasma species. The interplay between the plasma and the dust gives rise to behavior that is vastly different from that of
Tim Donders, Tim Staps, Job Beckers
doaj +2 more sources
Searches for high mass resonances with the CMS detector
EPJ Web of Conferences, 2012 New heavy resonances are predicted by many extensions of the standard model of particle physics. Recent results for high mass resonance searches with the Compact Muon Solenoid detector, in the diphoton, dilepton, dijet and tt¯ $tar t$ channels, are ...
Orimoto Toyoko J.
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New narrow N*(1685) resonance: Review of observations
EPJ Web of Conferences, 2014 The recent Review of Particle Physics [1] includes a new narrow N*(1685) resonance. Its properties, the narrow width (Γ < 25 MeV) and the strong photoexcitation on the neutron, are unusual.
Kuznetsov Viacheslav
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Anomaly Detection for Resonant New Physics with Machine Learning [PDF]
Physical Review Letters, 2018 Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics.
Collins, Jack H. +2 more
core +2 more sources
The neutron and its role in cosmology and particle physics [PDF]
Reviews of Modern Physics, 2011 Experiments with cold and ultracold neutrons have reached a level of precision such that problems far beyond the scale of the present standard model of particle physics become accessible to experimental investigation.
D Dubbers
exaly +2 more sources
Adversarially-trained autoencoders for robust unsupervised new physics searches [PDF]
Journal of High Energy Physics, 2019 Machine learning techniques in particle physics are most powerful when they are trained directly on data, to avoid sensitivity to theoretical uncertainties or an underlying bias on the expected signal.
Andrew Blance +2 more
doaj +2 more sources
Machine-Learning Compression for Particle Physics Discoveries [PDF]
arXiv.org, 2022 In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis.
J. Collins +4 more
semanticscholar +1 more source
The SuSA Model for Neutrino Oscillation Experiments: From Quasielastic Scattering to the Resonance Region [PDF]
Universe, 2021 High-precision studies of Beyond-Standard-Model physics through accelerator-based neutrino oscillation experiments require a very accurate description of neutrino–nucleus cross-sections in a broad energy region, going from quasielastic scattering up to ...
M. Barbaro, A. De Pace, L. Fiume
semanticscholar +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 +2 more
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Twisted particle collisions: A new tool for spin physics [PDF]
, 2020 Collisions of twisted particles --- that is, non-plane-wave states of photons, electrons, or any other particle, equipped with a non-zero orbital angular momentum (OAM) with respect to its propagation direction --- offer novel ways to probe particle ...
I. Ivanov +3 more
semanticscholar +1 more source