NA61/SHINE online noise filtering using machine learning methods
Paper i proceeding, 2023
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.
Författare
Anna Kawecka
Chalmers, Fysik, Subatomär, högenergi- och plasmafysik
Politechnika Warszawska
Wojciech Bryliński
Politechnika Warszawska
Manjunath Omana Kuttan
Frankfurt Institute for Advanced Studies
Johann Wolfgang Goethe Universität Frankfurt am Main
Olena Linnyk
Justus-Liebig-Universität Gießen
Frankfurt Institute for Advanced Studies
Milch&zucker AG
Janik Pawlowski
Philipps-Universität Marburg
Frankfurt Institute for Advanced Studies
Katarzyna Schmidt
University of Silesia in Katowice
Marcin Słodkowski
Politechnika Warszawska
Oskar Wyszyński
Akademia Swietokrzyska im. Jana Kochanowskiego w Kielcach
Jakub Zieliński
Politechnika Warszawska
Journal of Physics: Conference Series
17426588 (ISSN) 17426596 (eISSN)
Vol. 2438 1 012104
20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2021
Daejeon, Virtual, South Korea,
Daejeon, Virtual, South Korea,
Ämneskategorier
Acceleratorfysik och instrumentering
Subatomär fysik
Annan fysik
DOI
10.1088/1742-6596/2438/1/012104