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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F20%3A00537337" target="_blank" >RIV/68378271:_____/20:00537337 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21340/20:00344032 RIV/00216208:11320/20:10422129

Výsledek na webu

<a href="http://hdl.handle.net/11104/0315059" target="_blank" >http://hdl.handle.net/11104/0315059</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevD.102.092003" target="_blank" >10.1103/PhysRevD.102.092003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.n

Název v anglickém jazyce

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

Popis výsledku anglicky

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.n

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Physical Review D

ISSN

2470-0010

e-ISSN

—

Svazek periodika

102

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

000587596500004

EID výsledku v databázi Scopus

2-s2.0-85096669682