NA61/SHINE online noise filtering using machine learning methods

The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchan...

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Autores principales: Kawęcka, Anna, Bryliński, Wojciech, Omana Kuttan, Manjunath, Linnyk, Olena, Pawlowski, Janik, Schmidt, Katarzyna, Słodkowski, Marcin, Wyszyński, Oskar, Zieliński, Jakub
Lenguaje:eng
Publicado: 2023
Materias:
Detectors and Experimental Techniques
Computing and Computers
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/2438/1/012104
http://cds.cern.ch/record/2875016
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author Kawęcka, Anna
Bryliński, Wojciech
Omana Kuttan, Manjunath
Linnyk, Olena
Pawlowski, Janik
Schmidt, Katarzyna
Słodkowski, Marcin
Wyszyński, Oskar
Zieliński, Jakub
author_facet Kawęcka, Anna
Bryliński, Wojciech
Omana Kuttan, Manjunath
Linnyk, Olena
Pawlowski, Janik
Schmidt, Katarzyna
Słodkowski, Marcin
Wyszyński, Oskar
Zieliński, Jakub
author_sort Kawęcka, Anna
collection CERN
description The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers - TPCs). As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings.
id cern-2875016
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28750162023-10-09T13:34:52Zdoi:10.1088/1742-6596/2438/1/012104http://cds.cern.ch/record/2875016engKawęcka, AnnaBryliński, WojciechOmana Kuttan, ManjunathLinnyk, OlenaPawlowski, JanikSchmidt, KatarzynaSłodkowski, MarcinWyszyński, OskarZieliński, JakubNA61/SHINE online noise filtering using machine learning methodsDetectors and Experimental TechniquesComputing and ComputersThe NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers - TPCs). As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings.oai:cds.cern.ch:28750162023
spellingShingle Detectors and Experimental Techniques
Computing and Computers
Kawęcka, Anna
Bryliński, Wojciech
Omana Kuttan, Manjunath
Linnyk, Olena
Pawlowski, Janik
Schmidt, Katarzyna
Słodkowski, Marcin
Wyszyński, Oskar
Zieliński, Jakub
NA61/SHINE online noise filtering using machine learning methods
title NA61/SHINE online noise filtering using machine learning methods
title_full NA61/SHINE online noise filtering using machine learning methods
title_fullStr NA61/SHINE online noise filtering using machine learning methods
title_full_unstemmed NA61/SHINE online noise filtering using machine learning methods
title_short NA61/SHINE online noise filtering using machine learning methods
title_sort na61/shine online noise filtering using machine learning methods
topic Detectors and Experimental Techniques
Computing and Computers
url https://dx.doi.org/10.1088/1742-6596/2438/1/012104
http://cds.cern.ch/record/2875016
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