Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F24%3A00389343" target="_blank" >RIV/68407700:21670/24:00389343 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.22323/1.444.1194" target="_blank" >https://doi.org/10.22323/1.444.1194</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22323/1.444.1194" target="_blank" >10.22323/1.444.1194</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8

Popis výsledku v původním jazyce

KM3NeT is a research infrastructure hosting two large-volume Cherenkov neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector is optimised for the detection of high-energy neutrinos from astrophysical sources in the TeV-PeV energy range. Once completed, the detector will consist of 230 detection units. Here, we present a Deep Learning method using graph neural networks that is trained and applied to events gathered with 6 and 8 active detection units of KM3NeT/ARCA. Graph neural networks have been trained for classification and regression tasks, showing very promising performances in a range of different tasks like neutrino-background identification, neutrino event topology classification, energy and direction reconstruction, and also in the study of properties of muon bundles.

Název v anglickém jazyce

Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8

Popis výsledku anglicky

KM3NeT is a research infrastructure hosting two large-volume Cherenkov neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector is optimised for the detection of high-energy neutrinos from astrophysical sources in the TeV-PeV energy range. Once completed, the detector will consist of 230 detection units. Here, we present a Deep Learning method using graph neural networks that is trained and applied to events gathered with 6 and 8 active detection units of KM3NeT/ARCA. Graph neural networks have been trained for classification and regression tasks, showing very promising performances in a range of different tasks like neutrino-background identification, neutrino event topology classification, energy and direction reconstruction, and also in the study of properties of muon bundles.

Druh

D - Stať ve sborníku

CEP obor

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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)

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 (ICRC2023)

ISBN

—

ISSN

1824-8039

e-ISSN

1824-8039

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

PoS - Proceedings of Science, Sissa Medialab srl

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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