6533b839fe1ef96bd12a5dc0

RESEARCH PRODUCT

Neutrino interaction classification with a convolutional neural network in the DUNE far detector

Dune Collaboration

subject

luokitus (toiminta)neutriino-oskillaatiokoneoppiminenPhysics::Instrumentation and DetectorsAstrophysics::High Energy Astrophysical PhenomenaHigh Energy Physics::PhenomenologyneutriinotHigh Energy Physics::Experimentneuroverkothiukkasfysiikka

description

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 energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects. peerReviewed

yearjournalcountryeditionlanguage
2020-01-01
http://urn.fi/URN:NBN:fi:jyu-202012086973