Results 11 to 20 of about 1,477,419 (252)
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.
doaj +3 more sources
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
doaj +3 more sources
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
Physics Informed Neural Networks Applied to the Description of Wave-Particle Resonance in Kinetic Simulations of Fusion Plasmas [PDF]
, 2023 The Vlasov-Poisson system is employed in its reduced form version (1D1V) as a test bed for the applicability of Physics Informed Neural Network (PINN) to the wave-particle resonance. Two examples are explored: the Landau damping and the bump-on-tail instability.
Jai Kumar +4 more
+7 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
Probing Planckian physics: resonant production of particles during inflation and features in the primordial power spectrum [PDF]
Phys.Rev. D62 (2000) 043508, 1999 The phenomenon of resonant production of particles {\it after} inflation has received much attention in the past few years. In a new application of resonant production of particles, we consider the effect of a resonance {\em during} inflation. We show that if the inflaton is coupled to a massive particle, resonant production of the particle during ...
Edward W. Kolb +5 more
arxiv +4 more sources
L3 physics at the Z resonance and a search for the Higgs particle [PDF]
, 1997 This is the final report of a three-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory. Electroweak interactions were studied using the L3 Detector on the Large Electron-Positron Collider (LEP) at the European Center for Nuclear Study (CERN).
T. E. Coan +3 more
openalex +5 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