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

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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

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MLA
Samalan, A., et al. “A New Approach for CMS RPC Current Monitoring Using Machine Learning Techniques.” JOURNAL OF INSTRUMENTATION, vol. 15, no. 10, Iop Publishing Ltd, 2020, doi:10.1088/1748-0221/15/10/C10009.
APA
Samalan, A., Tytgat, M., Zaganidis, N., Alves, G. A., Marujo, F., Da Silva De Araujo, F. T., … Crottya, I. (2020). A new approach for CMS RPC current monitoring using Machine Learning techniques. JOURNAL OF INSTRUMENTATION, 15(10). https://doi.org/10.1088/1748-0221/15/10/C10009
Chicago author-date
Samalan, A., Michael Tytgat, Nikolaos Zaganidis, G. A. Alves, F. Marujo, F. Torres Da Silva De Araujo, E. M. Da Costa, et al. 2020. “A New Approach for CMS RPC Current Monitoring Using Machine Learning Techniques.” JOURNAL OF INSTRUMENTATION 15 (10). https://doi.org/10.1088/1748-0221/15/10/C10009.
Chicago author-date (all authors)
Samalan, A., Michael Tytgat, Nikolaos 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, and I Crottya. 2020. “A New Approach for CMS RPC Current Monitoring Using Machine Learning Techniques.” JOURNAL OF INSTRUMENTATION 15 (10). doi:10.1088/1748-0221/15/10/C10009.
Vancouver
1.
Samalan A, Tytgat M, Zaganidis N, Alves GA, Marujo F, Da Silva De Araujo FT, et al. A new approach for CMS RPC current monitoring using Machine Learning techniques. JOURNAL OF INSTRUMENTATION. 2020;15(10).
IEEE
[1]
A. Samalan et al., “A new approach for CMS RPC current monitoring using Machine Learning techniques,” JOURNAL OF INSTRUMENTATION, vol. 15, no. 10, 2020.
@article{8702610,
  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.}},
  articleno    = {{C10009}},
  author       = {{Samalan, A. and Tytgat, Michael and Zaganidis, Nikolaos and Alves, G. A. and Marujo, F. and Da Silva De Araujo, F. Torres and Da Costa, E. M. and De Jesus Damiao, D. and Nogima, H. and Santoro, A. and Fonseca De Souza, S. and Aleksandrov, A. and Hadjiiska, R. and Iaydjiev, P. and Rodozov, M. and Shopova, M. and Sultanov, G. and Bonchev, M. and Dimitrov, A. and Litov, L. and Pavlov, B. and Petkov, P. and Petrov, A. and Qian, S. J. and Bernal, C. and Cabrera, A. and Fraga, J. and Sarkar, A. and Elsayed, S. and Assran, Y. and El Sawy, M. and Mahmoud, M. A. and Mohammed, Y. and Combaret, C. and Gouzevitch, M. and Grenier, G. and Laktineh, I and Mirabito, L. and Shchablo, K. and Bagaturia, I and Lomidze, D. and Lomidze, I and Bhatnagar, V and Gupta, R. and Kumari, P. and Singh, J. and Amoozegar, V and Boghrati, B. and Ebraimi, M. and Ghasemi, R. and Najafabadi, M. Mohammadi and Zareian, E. and Abbrescia, M. and Aly, R. and Elmetenawee, W. and Filippis, N. and Gelmi, A. and Iaselli, G. and Leszki, S. and Loddo, F. and Margjeka, I and Pugliese, G. and Ramos, D. and Benussi, L. and Bianco, S. and Piccolo, D. and Buontempo, S. and Di Crescenzo, A. and Fienga, F. and De Lellis, G. and Lista, L. and Meola, S. and Paolucci, P. and Braghieri, A. and Salvini, P. and Montagna, P. and Riccardi, C. and Vitulo, P. and Francois, B. and Kim, T. J. and Park, J. and Choi, S. Y. and Hong, B. and Lee, K. S. and Goh, J. and Lee, H. and Eysermans, J. and Uribe Estrada, C. and Pedraza, I and Castilla-Valdez, H. and Sanchez-Hernandez, A. and Mondragon Herrera, C. A. and Perez Navarro, D. A. and Ayala Sanchez, G. A. and Carrillo, S. and Vazquez, E. and Radi, A. and Ahmad, A. and Asghar, I and Hoorani, H. and Muhammad, S. and Shah, M. A. and Crottya, I}},
  issn         = {{1748-0221}},
  journal      = {{JOURNAL OF INSTRUMENTATION}},
  keywords     = {{Large detector-systems performance,Resistive-plate chambers}},
  language     = {{eng}},
  location     = {{Univ Rome Tor Vergata, Rome, ITALY}},
  number       = {{10}},
  pages        = {{9}},
  publisher    = {{Iop Publishing Ltd}},
  title        = {{A new approach for CMS RPC current monitoring using Machine Learning techniques}},
  url          = {{http://dx.doi.org/10.1088/1748-0221/15/10/C10009}},
  volume       = {{15}},
  year         = {{2020}},
}

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