Multiplier-less approach in the neural network trigger algorithm for a detection of cosmic rays

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F19%3A00549098" target="_blank" >RIV/68378271:_____/19:00549098 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/CICN.2019.8902417" target="_blank" >https://doi.org/10.1109/CICN.2019.8902417</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CICN.2019.8902417" target="_blank" >10.1109/CICN.2019.8902417</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multiplier-less approach in the neural network trigger algorithm for a detection of cosmic rays

Popis výsledku v původním jazyce

Nowadays astrophysics is focused on understand the origin of the ultrahigh-energy cosmic rays (UHECR). Finding sources of UHECR is difficult, due to deflection of charged particles in intergalactic magnetic fields. This problem can be, however, avoided by detecting electrically neutral particles, such as neutrinos, which are created by the UHECR particles in interactions during propagation. Due to the very low cross section of the neutrinos, the detection technique requires a very sophisticated algorithm.Our trigger algorithm is based on an analysis of signal shapes by an artificial neural network (ANN). This approach can efficiently separate air showers which started at the top of the atmosphere.

Název v anglickém jazyce

Multiplier-less approach in the neural network trigger algorithm for a detection of cosmic rays

Popis výsledku anglicky

Nowadays astrophysics is focused on understand the origin of the ultrahigh-energy cosmic rays (UHECR). Finding sources of UHECR is difficult, due to deflection of charged particles in intergalactic magnetic fields. This problem can be, however, avoided by detecting electrically neutral particles, such as neutrinos, which are created by the UHECR particles in interactions during propagation. Due to the very low cross section of the neutrinos, the detection technique requires a very sophisticated algorithm.Our trigger algorithm is based on an analysis of signal shapes by an artificial neural network (ANN). This approach can efficiently separate air showers which started at the top of the atmosphere.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

—

Návaznosti

—

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 11th International Conference on Computational Intelligence and Communication Networks (CICN 2019)

ISBN

978-1-5386-8440-5

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

13-18

Název nakladatele

IEEE

Místo vydání

Danvers

Místo konání akce

Honolulu, HI

Datum konání akce

3. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—