Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F24%3A00381651" target="_blank" >RIV/68407700:21670/24:00381651 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.22323/1.444.1035" target="_blank" >https://doi.org/10.22323/1.444.1035</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22323/1.444.1035" target="_blank" >10.22323/1.444.1035</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6

Popis výsledku v původním jazyce

KM3NeT/ORCA is a large-volume water-Cherenkov neutrino detector, currently under construction at the bottom of the Mediterranean Sea at a depth of 2450 meters. The main research goal of ORCA is the measurement of the neutrino mass ordering and the atmospheric neutrino oscillation parameters. Additionally, the detector is also sensitive to a wide variety of phenomena including non-standard neutrino interactions, sterile neutrinos, and neutrino decay. This contribution describes the use of a machine learning framework for building Deep Neural Networks (DNN) which combine multiple energy estimates to generate a more precise reconstructed neutrino energy. The model is optimized to improve the oscillation analysis based on a data sample of 433 kton-years of KM3NeT/ORCA with 6 detection units. The performance of the model is evaluated by determining the sensitivity to oscillation parameters in comparison with the standard energy reconstruction method of maximizing a likelihood function. The results show that the DNN is able to provide a better energy estimate with lower bias in the context of oscillation analyses.

Název v anglickém jazyce

Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6

Popis výsledku anglicky

KM3NeT/ORCA is a large-volume water-Cherenkov neutrino detector, currently under construction at the bottom of the Mediterranean Sea at a depth of 2450 meters. The main research goal of ORCA is the measurement of the neutrino mass ordering and the atmospheric neutrino oscillation parameters. Additionally, the detector is also sensitive to a wide variety of phenomena including non-standard neutrino interactions, sterile neutrinos, and neutrino decay. This contribution describes the use of a machine learning framework for building Deep Neural Networks (DNN) which combine multiple energy estimates to generate a more precise reconstructed neutrino energy. The model is optimized to improve the oscillation analysis based on a data sample of 433 kton-years of KM3NeT/ORCA with 6 detection units. The performance of the model is evaluated by determining the sensitivity to oscillation parameters in comparison with the standard energy reconstruction method of maximizing a likelihood function. The results show that the DNN is able to provide a better energy estimate with lower bias in the context of oscillation analyses.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

<a href="/cs/project/LM2023063" target="_blank" >LM2023063: Podzemní laboratoř LSM - účast České republiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

38th International Cosmic Ray Conference

ISBN

—

ISSN

1824-8039

e-ISSN

1824-8039

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

SISSA-The International School for Advanced Studies

Místo vydání

Trieste

Místo konání akce

Nagoya

Datum konání akce

26. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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