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

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

doi:10.1088/1748-0221/15/10/C10009

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

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

Issue Date: Oct-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

URI: https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/2920

DOI: 10.1088/1748-0221/15/10/C10009

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.

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