A new approach for CMS RPC current monitoring using Machine Learning techniques

A. Samalan, M. Tytgat, N. Zaganidis, G. A. Alves, F. Marujo, F. Torres Da Silva De Araujo, E. M. da Costa, D. de Jesus Damiao, H. Nogima, A. Santoro, S. Fonseca De Souza, A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Rodozov, M. Shopova, G. Sultanov, M. Bonchev, A. Dimitrov, L. LitovB. Pavlov, P. Petkov*, A. Petrov, S. J. Qian, C. Bernal, A. Cabrera, J. Fraga, A. Sarkar, S. Elsayed, Y. Assran, M. El Sawy, M. A. Mahmoud, Y. Mohammed, C. Combaret, M. Gouzevitch, G. Grenier, I. Laktineh, L. Mirabito, K. Shchablo, I. Bagaturia, D. Lomidze, I. Lomidze, V. Bhatnagar, R. Gupta, P. Kumari, J. Singh, V. Amoozegar, B. Boghrati, M. Ebraimi, R. Ghasemi, M. Mohammadi Najafabadi, E. Zareian, M. Abbrescia, R. Aly, W. Elmetenawee, N. Filippis, A. Gelmi, G. Iaselli, S. Leszki, F. Loddo, I. Margjeka, G. Pugliese, D. Ramos, L. Benussi, S. Bianco, D. Piccolo, S. Buontempo, A. Di Crescenzo, F. Fienga, G. de Lellis, L. Lista, S. Meola, P. Paolucci, A. Braghieri, P. Salvini, P. Montagna, C. Riccardi, P. Vitulo, B. Francois, T. J. Kim, J. Park, S. Y. Choi, B. Hong, K. S. Lee, J. Goh, H. Lee, J. Eysermans, C. Uribe Estrada, I. Pedraza, H. Castilla-Valdez, A. Sanchez-Hernandez, C. A. Mondragon Herrera, D. A. Perez Navarro, G. A. Ayala Sanchez, S. Carrillo, E. Vazquez, A. Radi, A. Ahmad, I. Asghar, H. Hoorani, S. Muhammad, M. A. Shah, I. Crotty

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The CMS experiment has 1054 RPCs in its muon system. Monitoring their currents is the first essential step towards maintaining the stability of the CMS RPC detector performance. The current depends on several parameters such as applied voltage, luminosity, environmental conditions, etc. Knowing the influence of these parameters on the RPC current is essential for the correct interpretation of its instabilities as they can be caused either by changes in external conditions or by malfunctioning of the detector in the ideal case. We propose a Machine Learning(ML) based approach to be used for monitoring the CMS RPC currents. The approach is crucial for the development of an automated monitoring system capable of warning for possible hardware problems at a very early stage, which will contribute further to the stable operation of the CMS RPC detector.

Original languageEnglish
Article numberC10009
JournalJournal of Instrumentation
Volume15
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Large detector-systems performance
  • Resistive-plate chambers

ASJC Scopus subject areas

  • Instrumentation
  • Mathematical Physics

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