학술논문
Machine Learning based tool for CMS RPC currents quality monitoring
Document Type
Working Paper
Author
Shumka, E.; Samalan, A.; Tytgat, M.; Sawy, M. El; Alves, G. A.; Marujo, F.; Coelho, E. A.; Da Costa, E. M.; Nogima, H.; Santoro, A.; De Souza, S. Fonseca; Damiao, D. De Jesus; Thiel, M.; Amarilo, K. Mota; Filho, M. Barroso Ferreira; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Shopova, M.; Soultanov, G.; Dimitrov, A.; Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.; Qian, S. J.; Kou, H.; Liu, Z. -A.; Zhao, J.; Song, J.; Hou, Q.; Diao, W.; Cao, P.; Avila, C.; Barbosa, D.; Cabrera, A.; Florez, A.; Fraga, J.; Reyes, J.; Assran, Y.; Mahmoud, M. A.; Mohammed, Y.; Crotty, I.; Laktineh, I.; Grenier, G.; Gouzevitch, M.; Mirabito, L.; Shchablo, K.; Bagaturia, I.; Lomidze, I.; Tsamalaidze, Z.; Amoozegar, V.; Boghrati, B.; Ebraimi, M.; Najafabadi, M. Mohammadi; Zareian, E.; Abbrescia, M.; Iaselli, G.; Pugliese, G.; Loddo, F.; De Filippis, N.; Aly, R.; Ramos, D.; Elmetenawee, W.; Leszki, S.; Margjeka, I.; Paesani, D.; Benussi, L.; Bianco, S.; Piccolo, D.; Meola, S.; Buontempo, S.; Carnevali, F.; Lista, L.; Paolucci, P.; Fienga, F.; Braghieri, A.; Salvini, P.; Montagna, P.; Riccardi, C.; Vitulo, P.; Asilar, E.; Choi, J.; Kim, T. J.; Choi, S. Y.; Hong, B.; Lee, K. S.; Oh, H. Y.; Goh, J.; Yu, I.; Estrada, C. Uribe; Pedraza, I.; Castilla-Valdez, H.; Sanchez-Hernandez, A.; Fernandez, R. L.; Ramirez-Garcia, M.; Vazquez, E.; Shah, M. A.; Zaganidis, N.; Radi, A.; Hoorani, H.; Muhammad, S.; Ahmad, A.; Asghar, I.; Khan, W. A.; Eysermans, J.; De Araujo, F. Torres Da Silva
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Abstract
The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to $2\times 10^{34}$ $\text{cm}^{-2}\text{s}^{-1}$ are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future.