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A new approach for CMS RPC current monitoring using Machine Learning techniques
Journal of Instrumentation ( IF 1.3 ) Pub Date : 2020-10-07 , DOI: 10.1088/1748-0221/15/10/c10009 A. Samalan 1 , M. Tytgat 1 , N. Zaganidis 1 , G.A. Alves 2 , F. Marujo 2 , F. Torres Da Silva De Araujo 3 , E.M. Da Costa 3 , D. De Jesus Damiao 3 , H. Nogima 3 , A. Santoro 3 , S. Fonseca De Souza 3 , A. Aleksandrov 4 , R. Hadjiiska 4 , P. Iaydjiev 4 , M. Rodozov 4 , M. Shopova 4 , G. Sultanov 4 , M. Bonchev 5 , A. Dimitrov 5 , L. Litov 5 , B. Pavlov 5 , P. Petkov 5 , A. Petrov 5 , S.J. Qian 6 , C. Bernal 7 , A. Cabrera 7 , J. Fraga 7 , A. Sarkar 7 , S. Elsayed 8 , Y. Assran 9, 10 , M. El Sawy 9, 11 , M.A. Mahmoud 12 , Y. Mohammed 12 , C. Combaret 13 , M. Gouzevitch 13 , G. Grenier 13 , I. Laktineh 13 , L. Mirabito 13 , K. Shchablo 13 , I. Bagaturia 14 , D. Lomidze 14 , I. Lomidze 14 , V. Bhatnagar 15 , R. Gupta 15 , P. Kumari 15 , J. Singh 15 , V. Amoozegar 16 , B. Boghrati 16, 17 , M. Ebraimi 16 , R. Ghasemi 16 , M. Mohammadi Najafabadi 16 , E. Zareian 16 , M. Abbrescia 18 , R. Aly 18 , W. Elmetenawee 18 , N. Filippis 18 , A. Gelmi 18 , G. Iaselli 18 , S. Leszki 18 , F. Loddo 18 , I. Margjeka 18 , G. Pugliese 18 , D. Ramos 18 , L. Benussi 19 , S. Bianco 19 , D. Piccolo 19 , S. Buontempo 20 , A. Di Crescenzo 20 , F. Fienga 20 , G. De Lellis 20 , L. Lista 20 , S. Meola 20 , P. Paolucci 20 , A. Braghieri 21 , P. Salvini 21 , P. Montagna 22 , C. Riccardi 22 , P. Vitulo 22 , B. Francois 23 , T.J. Kim 23 , J. Park 23 , S.Y. Choi 24 , B. Hong 24 , K.S. Lee 24 , J. Goh 25 , H. Lee 26 , J. Eysermans 27 , C. Uribe Estrada 27 , I. Pedraza 27 , H. Castilla-Valdez 28 , A. Sanchez-Hernandez 28 , C.A. Mondragon Herrera 28 , D.A. Perez Navarro 28 , G.A. Ayala Sanchez 28 , S. Carrillo 29 , E. Vazquez 29 , A. Radi 30 , A. Ahmad 31 , I. Asghar 31 , H. Hoorani 31 , S. Muhammad 31 , M.A. Shah 31 , I. Crotty 32
Journal of Instrumentation ( IF 1.3 ) Pub Date : 2020-10-07 , DOI: 10.1088/1748-0221/15/10/c10009 A. Samalan 1 , M. Tytgat 1 , N. Zaganidis 1 , G.A. Alves 2 , F. Marujo 2 , F. Torres Da Silva De Araujo 3 , E.M. Da Costa 3 , D. De Jesus Damiao 3 , H. Nogima 3 , A. Santoro 3 , S. Fonseca De Souza 3 , A. Aleksandrov 4 , R. Hadjiiska 4 , P. Iaydjiev 4 , M. Rodozov 4 , M. Shopova 4 , G. Sultanov 4 , M. Bonchev 5 , A. Dimitrov 5 , L. Litov 5 , B. Pavlov 5 , P. Petkov 5 , A. Petrov 5 , S.J. Qian 6 , C. Bernal 7 , A. Cabrera 7 , J. Fraga 7 , A. Sarkar 7 , S. Elsayed 8 , Y. Assran 9, 10 , M. El Sawy 9, 11 , M.A. Mahmoud 12 , Y. Mohammed 12 , C. Combaret 13 , M. Gouzevitch 13 , G. Grenier 13 , I. Laktineh 13 , L. Mirabito 13 , K. Shchablo 13 , I. Bagaturia 14 , D. Lomidze 14 , I. Lomidze 14 , V. Bhatnagar 15 , R. Gupta 15 , P. Kumari 15 , J. Singh 15 , V. Amoozegar 16 , B. Boghrati 16, 17 , M. Ebraimi 16 , R. Ghasemi 16 , M. Mohammadi Najafabadi 16 , E. Zareian 16 , M. Abbrescia 18 , R. Aly 18 , W. Elmetenawee 18 , N. Filippis 18 , A. Gelmi 18 , G. Iaselli 18 , S. Leszki 18 , F. Loddo 18 , I. Margjeka 18 , G. Pugliese 18 , D. Ramos 18 , L. Benussi 19 , S. Bianco 19 , D. Piccolo 19 , S. Buontempo 20 , A. Di Crescenzo 20 , F. Fienga 20 , G. De Lellis 20 , L. Lista 20 , S. Meola 20 , P. Paolucci 20 , A. Braghieri 21 , P. Salvini 21 , P. Montagna 22 , C. Riccardi 22 , P. Vitulo 22 , B. Francois 23 , T.J. Kim 23 , J. Park 23 , S.Y. Choi 24 , B. Hong 24 , K.S. Lee 24 , J. Goh 25 , H. Lee 26 , J. Eysermans 27 , C. Uribe Estrada 27 , I. Pedraza 27 , H. Castilla-Valdez 28 , A. Sanchez-Hernandez 28 , C.A. Mondragon Herrera 28 , D.A. Perez Navarro 28 , G.A. Ayala Sanchez 28 , S. Carrillo 29 , E. Vazquez 29 , A. Radi 30 , A. Ahmad 31 , I. Asghar 31 , H. Hoorani 31 , S. Muhammad 31 , M.A. Shah 31 , I. Crotty 32
Affiliation
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.
中文翻译：
一种使用机器学习技术进行 CMS RPC 电流监控的新方法
CMS 实验在其介子系统中有 1054 个 RPC。监测它们的电流是保持 CMS RPC 检测器性能稳定性的第一个重要步骤。电流取决于几个参数，例如施加的电压、光度、环境条件等。了解这些参数对 RPC 电流的影响对于正确解释其不稳定性至关重要，因为它们可能由外部条件的变化或由理想情况下探测器故障。我们提出了一种基于机器学习（ML）的方法，用于监控 CMS RPC 电流。该方法对于开发能够在早期阶段对可能的硬件问题发出警告的自动监控系统至关重要，这将进一步有助于 CMS RPC 检测器的稳定运行。
更新日期：2020-10-07
中文翻译：
一种使用机器学习技术进行 CMS RPC 电流监控的新方法
CMS 实验在其介子系统中有 1054 个 RPC。监测它们的电流是保持 CMS RPC 检测器性能稳定性的第一个重要步骤。电流取决于几个参数，例如施加的电压、光度、环境条件等。了解这些参数对 RPC 电流的影响对于正确解释其不稳定性至关重要，因为它们可能由外部条件的变化或由理想情况下探测器故障。我们提出了一种基于机器学习（ML）的方法，用于监控 CMS RPC 电流。该方法对于开发能够在早期阶段对可能的硬件问题发出警告的自动监控系统至关重要，这将进一步有助于 CMS RPC 检测器的稳定运行。
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