0000000000236306
AUTHOR
J. Fried
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino…
Erratum to: The Intensive Care Global Study on Severe Acute Respiratory Infection (ICâGLOSSARI): a multicenter, multinational, 14-day inception cohort study (Intensive Care Medicine, (2016), 42, 5, (953), 10.1007/s00134-016-4317-4)
In both the original publication (DOI 10.1007/s00134-015-4206-2) and the first erratum (DOI 10.1007/s00134-016-4317-4), the members of the IC-GLOSSARI Investigators and the ESICM Trials Group were provided in such a way that they could not be indexed as collaborators on PubMed. The publisher apologizes for these errors and is pleased to list the members of the groups here: (Table presented.).