Machine Learning based tool for CMS RPC currents quality monitoring

被引：1

作者：

Shumka, E. ^{[6
]}

Samalan, A. ^{[1
]}

Tytgat, M. ^{[1
]}

El Sawy, M. ^{[2
]}

Alves, G. A. ^{[3
]}

Marujo, F. ^{[3
]}

Coelho, E. A. ^{[3
]}

Da Costa, E. M. ^{[4
]}

Nogima, H. ^{[4
]}

Santoro, A. ^{[4
]}

De Souza, S. Fonseca ^{[4
]}

Damiao, D. De Jesus ^{[4
]}

Thiel, M. ^{[4
]}

Amarilo, K. Mota ^{[4
]}

Ferreira Filho, M. Barroso ^{[4
]}

Aleksandrov, A. ^{[5
]}

Hadjiiska, R. ^{[5
]}

Iaydjiev, P. ^{[5
]}

Rodozov, M. ^{[5
]}

Shopova, M. ^{[5
]}

Soultanov, G. ^{[5
]}

Dimitrov, A. ^{[6
]}

Litov, L. ^{[6
]}

Pavlov, B. ^{[6
]}

Petkov, P. ^{[6
]}

Petrov, A. ^{[6
]}

Qian, S. J. ^{[7
]}

Kou, H. ^{[8
,9
]}

Liu, Z. -A. ^{[8
,9
]}

Zhao, J. ^{[8
,9
]}

Song, J. ^{[8
,9
]}

Hou, Q. ^{[8
,9
]}

Diao, W. ^{[8
,9
]}

Cao, P. ^{[8
,9
]}

Avila, C. ^{[10
]}

Barbosa, D. ^{[10
]}

Cabrera, A. ^{[10
]}

Florez, A. ^{[10
]}

Fraga, J. ^{[10
]}

Reyes, J. ^{[10
]}

Assran, Y. ^{[11
,12
]}

Mahmoud, M. A. ^{[13
]}

Mohammed, Y. ^{[13
]}

Crotty, I. ^{[13
]}

Laktineh, I. ^{[15
]}

Grenier, G. ^{[15
]}

Gouzevitch, M. ^{[15
]}

Mirabito, L. ^{[15
]}

Shchablo, K. ^{[15
]}

Bagaturia, I. ^{[16
]}

机构：

[1] Univ Ghent, Dept Phys & Astron, Proeftuinstr 86, B-9000 Ghent, Belgium

[2] Univ Libre Bruxelles, Ave Franklin Roosevelt 50, B-1050 Brussels, Belgium

[3] Ctr Brasileiro Pesquisas Fisicas, R Dr Xavier Sigaud,150 Urca, BR-22290180 Rio De Janeiro, Brazil

[4] Univ Estado Rio De Janeiro, Inst Fis, Dep Fis Nucl & Altas Energias, Rua Sao Francisco Xavier,524, BR-20559900 Rio De Janeiro, RJ, Brazil

[5] Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Tzarigradsko Shaussee Blvd 72, BG-1784 Sofia, Bulgaria

[6] Univ Sofia, Fac Phys, 5 James Bourchier Blvd, BG-1164 Sofia, Bulgaria

[7] Peking Univ, Sch Phys, Beijing 100871, Peoples R China

[8] Chinese Acad Sci, Inst High Energy Phys, State Key Lab Particle Detect & Elect, Beijing 100049, Peoples R China

[9] Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China

[10] Univ Los Andes, Carrera 1,18A 12, Bogota, Colombia

[11] Acad Sci Res & Technol, Egyptian Network High Energy Phys, 101 Kasr El Einy St, Cairo, Egypt

[12] Suez Univ, Suez Cairo Rd, Suez 43522, Egypt

[13] Fayoum Univ, Ctr High Energy Phys CHEP FU, Fac Sci, Al Fayyum 63514, Egypt

[14] Ain Shams Univ, Dept Phys, Fac Sci, Cairo, Egypt

[15] Univ Lyon, Univ Claude Bernard Lyon 1, CNRS, IN2P3,IP2I Lyon,UMR 5822, F-69622 Lyon, France

[16] Georgian Tech Univ, 77 Kostava Str, Tbilisi 0175, Georgia

[17] Inst Res Fundamental Sci IPM, Sch Part & Accelerators, POB 19395-5531, Tehran, Iran

[18] Damghan Univ, Sch Engn, Damghan 3671641167, Iran

[19] INFN, Sezione Bari, Via Orabona 4, IT-70126 Bari, Italy

[20] INFN, Lab Nazl Frascati LNF, Via Enrico Fermi 40, IT-00044 Frascati, Italy

[21] Complesso Univ Monte S Angelo, Sez Napoli, INFN, Via Cintia, IT-80126 Naples, Italy

[22] Univ Napoli Federico II, Dipartimento Ingn Elettr & Tecnol Informaz, IT-80126 Naples, Italy

[23] INFN, Sez Pavia, Via Bassi 6, Pavia, Italy

[24] Univ Pavia, Via Bassi 6, Pavia, Italy

[25] Hanyang Univ, 222 Wangsimni Ro,Sageun Dong, Seoul, South Korea

[26] Korea Univ, Dept Phys, 145 Anam Ro, Seoul 02841, South Korea

[27] Kyung Hee Univ, 26 Kyungheedae Ro, Seoul 02447, South Korea

[28] Sungkyunkwan Univ, 2066 Seobu Ro, Suwon 16419, South Korea

[29] Benemerita Univ Autonoma Puebla, Puebla, Mexico

[30] CINVESTAV, Ave Inst Politecn Nacl 2508,Colonia San Pedro, Mexico City 07360, DF, Mexico

[31] Univ beroamericana, Mexico City, DF, Mexico

[32] Sultan Qaboos Univ, Muscat 123, Oman

[33] Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan

[34] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[35] Rhein Westfal TH Aachen, III Physikal Inst A, Sommerfeldstr D-52056, Aachen, Germany

[36] Helwan Univ, Dept Phys, Fac Sci, Ain Helwan 11795, Cairo, Egypt

来源：

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT | 2023年 / 1054卷

关键词：

CMS experiment; Resistive Plate Chambers; Machine Learning; Gas detectors; Monitoring tools;

D O I：

10.1016/j.nima.2023.168449

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

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.

引用

页数：6

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