A comparative analysis of machine learning techniques for muon count in UHECR extensive air-showers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F20%3A00546314" target="_blank" >RIV/68378271:_____/20:00546314 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/e22111216" target="_blank" >https://doi.org/10.3390/e22111216</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/e22111216" target="_blank" >10.3390/e22111216</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A comparative analysis of machine learning techniques for muon count in UHECR extensive air-showers

Popis výsledku v původním jazyce

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.

Název v anglickém jazyce

A comparative analysis of machine learning techniques for muon count in UHECR extensive air-showers

Popis výsledku anglicky

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

—

Návaznosti

—

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Entropy

ISSN

1099-4300

e-ISSN

1099-4300

Svazek periodika

22

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

17

Strana od-do

1216

Kód UT WoS článku

000592764300001

EID výsledku v databázi Scopus

2-s2.0-85094565101