Machine Learning based tool for CMS RPC currents quality monitoring

  • Shumka, E.
  • Samalan, A.
  • Tytgat, M.
  • El Sawy, M.
  • Alves, G. A.
  • ... Kim, T. J.
  • 외 102명
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초록

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 2x1034 cm-2s-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.

키워드

CMS experimentResistive Plate ChambersMachine LearningGas detectorsMonitoring toolsCompact muon solenoid experimentCompact Muon solenoidsCurrent qualityGaseous detectorsHostile environmentsMachine-learningMonitoring toolsMuon systemsQuality monitoringResistive plates chambers
제목
Machine Learning based tool for CMS RPC currents quality monitoring
저자
Shumka, E.Samalan, A.Tytgat, M.El Sawy, M.Alves, G. A.Marujo, F.Coelho, E. A.Da Costa, E. M.Nogima, H.Santoro, A.De Souza, S. FonsecaDamiao, D. De JesusThiel, M.Amarilo, K. MotaFerreira Filho, M. BarrosoAleksandrov, 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. MohammadiZareian, 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. UribePedraza, 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
DOI
10.1016/j.nima.2023.168449
발행일
2023-09
유형
Article
저널명
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
1054
페이지
1 ~ 6

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