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. Litov; B. 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

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

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

^*المؤلف المقابل لهذا العمل

Physics

نتاج البحث: المساهمة في مجلة › Article › مراجعة النظراء

1 اقتباس (Scopus)

ملخص

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.

اللغة الأصلية	English
رقم المقال	C10009
دورية	Journal of Instrumentation
مستوى الصوت	15
رقم الإصدار	10
المعرِّفات الرقمية للأشياء	https://doi.org/10.1088/1748-0221/15/10/C10009
حالة النشر	Published - أكتوبر 2020

ASJC Scopus subject areas

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???subjectarea.asjc.2600.2610???

الوصول إلى المستند

10.1088/1748-0221/15/10/C10009

الملفات والروابط الأخرى

قم بذكر هذا

Samalan, A., Tytgat, M., Zaganidis, N., Alves, G. A., Marujo, F., Torres Da Silva De Araujo, F., 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., ... Crotty, I. (2020). A new approach for CMS RPC current monitoring using Machine Learning techniques. Journal of Instrumentation, 15(10), المقال C10009. https://doi.org/10.1088/1748-0221/15/10/C10009

Samalan, A, Tytgat, M, Zaganidis, N, Alves, GA, Marujo, F, Torres Da Silva De Araujo, F, da Costa, EM, 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, SJ, Bernal, C, Cabrera, A, Fraga, J, Sarkar, A, Elsayed, S, Assran, Y, El Sawy, M, Mahmoud, MA, 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, Mohammadi Najafabadi, M, 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, TJ, Park, J, Choi, SY, Hong, B, Lee, KS, Goh, J, Lee, H, Eysermans, J, Uribe Estrada, C, Pedraza, I, Castilla-Valdez, H, Sanchez-Hernandez, A, Mondragon Herrera, CA, Perez Navarro, DA, Ayala Sanchez, GA, Carrillo, S, Vazquez, E, Radi, A, Ahmad, A, Asghar, I, Hoorani, H, Muhammad, S, Shah, MA & Crotty, I 2020, 'A new approach for CMS RPC current monitoring using Machine Learning techniques', Journal of Instrumentation, المجلد 15, رقم 10, C10009. https://doi.org/10.1088/1748-0221/15/10/C10009

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title = "A new approach for CMS RPC current monitoring using Machine Learning techniques",

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.",

keywords = "Large detector-systems performance, Resistive-plate chambers",

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

note = "Publisher Copyright: {\textcopyright} 2020 IOP Publishing Ltd and Sissa Medialab",

year = "2020",

month = oct,

doi = "10.1088/1748-0221/15/10/C10009",

language = "English",

volume = "15",

journal = "Journal of Instrumentation",

issn = "1748-0221",

publisher = "IOP Publishing Ltd.",

number = "10",

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TY - JOUR

T1 - A new approach for CMS RPC current monitoring using Machine Learning techniques

AU - Samalan, A.

AU - Tytgat, M.

AU - Zaganidis, N.

AU - Alves, G. A.

AU - Marujo, F.

AU - Torres Da Silva De Araujo, F.

AU - da Costa, E. M.

AU - de Jesus Damiao, D.

AU - Nogima, H.

AU - Santoro, A.

AU - Fonseca De Souza, S.

AU - Aleksandrov, A.

AU - Hadjiiska, R.

AU - Iaydjiev, P.

AU - Rodozov, M.

AU - Shopova, M.

AU - Sultanov, G.

AU - Bonchev, M.

AU - Dimitrov, A.

AU - Litov, L.

AU - Pavlov, B.

AU - Petkov, P.

AU - Petrov, A.

AU - Qian, S. J.

AU - Bernal, C.

AU - Cabrera, A.

AU - Fraga, J.

AU - Sarkar, A.

AU - Elsayed, S.

AU - Assran, Y.

AU - El Sawy, M.

AU - Mahmoud, M. A.

AU - Mohammed, Y.

AU - Combaret, C.

AU - Gouzevitch, M.

AU - Grenier, G.

AU - Laktineh, I.

AU - Mirabito, L.

AU - Shchablo, K.

AU - Bagaturia, I.

AU - Lomidze, D.

AU - Lomidze, I.

AU - Bhatnagar, V.

AU - Gupta, R.

AU - Kumari, P.

AU - Singh, J.

AU - Amoozegar, V.

AU - Boghrati, B.

AU - Ebraimi, M.

AU - Ghasemi, R.

AU - Mohammadi Najafabadi, M.

AU - Zareian, E.

AU - Abbrescia, M.

AU - Aly, R.

AU - Elmetenawee, W.

AU - Filippis, N.

AU - Gelmi, A.

AU - Iaselli, G.

AU - Leszki, S.

AU - Loddo, F.

AU - Margjeka, I.

AU - Pugliese, G.

AU - Ramos, D.

AU - Benussi, L.

AU - Bianco, S.

AU - Piccolo, D.

AU - Buontempo, S.

AU - Di Crescenzo, A.

AU - Fienga, F.

AU - de Lellis, G.

AU - Lista, L.

AU - Meola, S.

AU - Paolucci, P.

AU - Braghieri, A.

AU - Salvini, P.

AU - Montagna, P.

AU - Riccardi, C.

AU - Vitulo, P.

AU - Francois, B.

AU - Kim, T. J.

AU - Park, J.

AU - Choi, S. Y.

AU - Hong, B.

AU - Lee, K. S.

AU - Goh, J.

AU - Lee, H.

AU - Eysermans, J.

AU - Uribe Estrada, C.

AU - Pedraza, I.

AU - Castilla-Valdez, H.

AU - Sanchez-Hernandez, A.

AU - Mondragon Herrera, C. A.

AU - Perez Navarro, D. A.

AU - Ayala Sanchez, G. A.

AU - Carrillo, S.

AU - Vazquez, E.

AU - Radi, A.

AU - Ahmad, A.

AU - Asghar, I.

AU - Hoorani, H.

AU - Muhammad, S.

AU - Shah, M. A.

AU - Crotty, I.

PY - 2020/10

Y1 - 2020/10

N2 - 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.

AB - 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.

KW - Large detector-systems performance

KW - Resistive-plate chambers

UR - http://www.scopus.com/inward/record.url?scp=85092630108&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85092630108&partnerID=8YFLogxK

U2 - 10.1088/1748-0221/15/10/C10009

DO - 10.1088/1748-0221/15/10/C10009

M3 - Article

AN - SCOPUS:85092630108

SN - 1748-0221

VL - 15

JO - Journal of Instrumentation

JF - Journal of Instrumentation

IS - 10

M1 - C10009

ER -