Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
A new approach for CMS RPC current monitoring using Machine Learning techniques
- Authors
- Samalan, A.; Tytgat, M.; Zaganidis, N.; Alves, G. A.; Marujo, F.; Da Silva De Araujo, F. Torres; Da Costa, E. M.; De Jesus Damiao, D.; Nogima, H.; Santoro, A.; Fonseca De Souza, S.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Shopova, M.; Sultanov, G.; Bonchev, M.; Dimitrov, A.; Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.; Qian, S. J.; Bernal, C.; Cabrera, A.; Fraga, J.; Sarkar, A.; Elsayed, S.; Assran, Y.; El Sawy, M.; Mahmoud, M. A.; Mohammed, Y.; Combaret, C.; Gouzevitch, M.; Grenier, G.; Laktineh, I; Mirabito, L.; Shchablo, K.; Bagaturia, I; Lomidze, D.; Lomidze, I; Bhatnagar, V; Gupta, R.; Kumari, P.; Singh, J.; Amoozegar, V; Boghrati, B.; Ebraimi, M.; Ghasemi, R.; Najafabadi, M. Mohammadi; Zareian, E.; Abbrescia, M.; Aly, R.; Elmetenawee, W.; Filippis, N.; Gelmi, A.; Iaselli, G.; Leszki, S.; Loddo, F.; Margjeka, I; Pugliese, G.; Ramos, D.; Benussi, L.; Bianco, S.; Piccolo, D.; Buontempo, S.; Di Crescenzo, A.; Fienga, F.; De Lellis, G.; Lista, L.; Meola, S.; Paolucci, P.; Braghieri, A.; Salvini, P.; Montagna, P.; Riccardi, C.; Vitulo, P.; Francois, B.; Kim, T. J.; Park, J.; Choi, S. Y.; Hong, B.; Lee, K. S.; Goh, J.; Lee, H.; Eysermans, J.; Uribe Estrada, C.; Pedraza, I; Castilla-Valdez, H.; Sanchez-Hernandez, A.; Mondragon Herrera, C. A.; Perez Navarro, D. A.; Ayala Sanchez, G. A.; Carrillo, S.; Vazquez, E.; Radi, A.; Ahmad, A.; Asghar, I; Hoorani, H.; Muhammad, S.; Shah, M. A.; Crottya, I
- Issue Date
- 10월-2020
- Publisher
- IOP PUBLISHING LTD
- Keywords
- Large detector-systems performance; Resistive-plate chambers
- Citation
- JOURNAL OF INSTRUMENTATION, v.15, no.10
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF INSTRUMENTATION
- Volume
- 15
- Number
- 10
- ISSN
- 1748-0221
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Science > Department of Physics > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.