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

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Identification of Beam Particles Using Detectors based on Cerenkov effect and Machine Learning in the COMPASS Experiment at CERN
Original language description
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)
Czech name
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Czech description
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Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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