Identification of Beam Particles Using Detectors based on Cerenkov effect and Machine Learning in the COMPASS Experiment at CERN

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F22%3A00374235" target="_blank" >RIV/68407700:21340/22:00374235 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Identification of Beam Particles Using Detectors based on Cerenkov effect and Machine Learning in the COMPASS Experiment at CERN

Popis výsledku v původním jazyce

Cerenkov Differential counters with Achromatic Ring focus (CEDARs) in the COMPASS experiment beamline were designed to identify particles in limited intensity beams with divergence below 65μrad. However, in the 2018 data taking, a beam with a 15 times higher intensity and a beam divergence of up to 300μrad was used, hence the standard data analysis method could not be used. A machine learning approach using neural networks was developed and examined on multiple Monte Carlo simulations. Different types of network were tested and their configurations optimized using a genetic algorithm with the best performing model being integrated into the current data analysis software written in C++. Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

Název v anglickém jazyce

Identification of Beam Particles Using Detectors based on Cerenkov effect and Machine Learning in the COMPASS Experiment at CERN

Popis výsledku anglicky

Cerenkov Differential counters with Achromatic Ring focus (CEDARs) in the COMPASS experiment beamline were designed to identify particles in limited intensity beams with divergence below 65μrad. However, in the 2018 data taking, a beam with a 15 times higher intensity and a beam divergence of up to 300μrad was used, hence the standard data analysis method could not be used. A machine learning approach using neural networks was developed and examined on multiple Monte Carlo simulations. Different types of network were tested and their configurations optimized using a genetic algorithm with the best performing model being integrated into the current data analysis software written in C++. Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10303 - Particles and field physics

Projekt

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Návaznosti

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Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of Science

ISBN

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ISSN

1824-8039

e-ISSN

1824-8039

Počet stran výsledku

6

Strana od-do

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Název nakladatele

Sissa Medialab Srl

Místo vydání

Trieste

Místo konání akce

Bologna

Datum konání akce

6. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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