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arXiv 2302.02764physics.ins-dethep-ex
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基于机器学习的CMS RPC电流质量监测工具

Machine Learning based tool for CMS RPC currents quality monitoring

E. Shumka, A. Samalan, M. Tytgat, M. El Sawy, G. A. Alves, F. Marujo, E. A. Coelho, E. M. Da Costa, H. Nogima, A. Santoro, S. Fonseca De Souza, D. De Jesus Dami… 展开作者

E. Shumka, A. Samalan, M. Tytgat, M. El Sawy, G. A. Alves, F. Marujo, E. A. Coelho, E. M. Da Costa, H. Nogima, A. Santoro, S. Fonseca De Souza, D. De Jesus Damiao, M. Thiel, K. Mota Amarilo, M. Barroso Ferreira Filho, A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Rodozov, M. Shopova, G. Soultanov, A. Dimitrov, L. Litov, B. Pavlov, P. Petkov, A. Petrov, S. J. Qian, H. Kou, Z. -A. Liu, J. Zhao, J. Song, Q. Hou, W. Diao, P. Cao, C. Avila, D. Barbosa, A. Cabrera, A. Florez, J. Fraga, J. Reyes, Y. Assran, M. A. Mahmoud, Y. Mohammed, I. Crotty, I. Laktineh, G. Grenier, M. Gouzevitch, L. Mirabito, K. Shchablo, I. Bagaturia, I. Lomidze, Z. Tsamalaidze, V. Amoozegar, B. Boghrati, M. Ebraimi, M. Mohammadi Najafabadi, E. Zareian, M. Abbrescia, G. Iaselli, G. Pugliese, F. Loddo, N. De Filippis, R. Aly, D. Ramos, W. Elmetenawee, S. Leszki, I. Margjeka, D. Paesani, L. Benussi, S. Bianco, D. Piccolo, S. Meola, S. Buontempo, F. Carnevali, L. Lista, P. Paolucci, F. Fienga, A. Braghieri, P. Salvini, P. Montagna, C. Riccardi, P. Vitulo, E. Asilar, J. Choi, T. J. Kim, S. Y. Choi, B. Hong, K. S. Lee, H. Y. Oh, J. Goh, I. Yu, C. Uribe Estrada, I. Pedraza, H. Castilla-Valdez, A. Sanchez-Hernandez, R. L. Fernandez, M. Ramirez-Garcia, E. Vazquez, M. A. Shah, N. Zaganidis, A. Radi, H. Hoorani, S. Muhammad, A. Ahmad, I. Asghar, W. A. Khan, J. Eysermans, F. Torres Da Silva De Araujo

AI总结:

针对CMS实验RPC探测器电流监测需求,研究团队开发了基于机器学习的自动监测工具,在原有广义线性模型基础上新增自编码器与混合模型,支持自洽预测及远期电流预测,助力探测器稳定运行。

摘要:

欧洲核子研究中心(CERN)紧凑型μ子螺线管(CMS)实验的μ子系统包含一千多个阻性板室(Resistive Plate Chambers, RPC)。这些气体探测器运行在大型强子对撞机上CMS地下洞穴的恶劣环境中,该环境下质子-质子(pp)亮度常规可达$2\ imes 10^{34}$ $\ ext{cm}^{-2}\ ext{s}^{-1}$。研究人员持续监测CMS RPC系统的性能,并定期维护探测器以确保稳定运行,主要监测特征包括暗电流、μ子探测效率、噪声率等。本文介绍了一种采用机器学习技术的CMS RPC电流自动监测工具,进一步详述了此前提出的专用广义线性模型,新增了用于自洽预测的自编码器模型,以及可实现远期RPC电流预测的混合模型。

英文摘要:

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 $2\times 10^{34}$ $\text{cm}^{-2}\text{s}^{-1}$ 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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