Machine Learning based tool for CMS RPC currents quality monitoring
Abstract
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 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.
Cite
@article{arxiv.2302.02764,
title = {Machine Learning based tool for CMS RPC currents quality monitoring},
author = {E. Shumka and A. Samalan and M. Tytgat and M. El Sawy and G. A. Alves and F. Marujo and E. A. Coelho and E. M. Da Costa and H. Nogima and A. Santoro and S. Fonseca De Souza and D. De Jesus Damiao and M. Thiel and K. Mota Amarilo and M. Barroso Ferreira Filho and A. Aleksandrov and R. Hadjiiska and P. Iaydjiev and M. Rodozov and M. Shopova and G. Soultanov and A. Dimitrov and L. Litov and B. Pavlov and P. Petkov and A. Petrov and S. J. Qian and H. Kou and Z. -A. Liu and J. Zhao and J. Song and Q. Hou and W. Diao and P. Cao and C. Avila and D. Barbosa and A. Cabrera and A. Florez and J. Fraga and J. Reyes and Y. Assran and M. A. Mahmoud and Y. Mohammed and I. Crotty and I. Laktineh and G. Grenier and M. Gouzevitch and L. Mirabito and K. Shchablo and I. Bagaturia and I. Lomidze and Z. Tsamalaidze and V. Amoozegar and B. Boghrati and M. Ebraimi and M. Mohammadi Najafabadi and E. Zareian and M. Abbrescia and G. Iaselli and G. Pugliese and F. Loddo and N. De Filippis and R. Aly and D. Ramos and W. Elmetenawee and S. Leszki and I. Margjeka and D. Paesani and L. Benussi and S. Bianco and D. Piccolo and S. Meola and S. Buontempo and F. Carnevali and L. Lista and P. Paolucci and F. Fienga and A. Braghieri and P. Salvini and P. Montagna and C. Riccardi and P. Vitulo and E. Asilar and J. Choi and T. J. Kim and S. Y. Choi and B. Hong and K. S. Lee and H. Y. Oh and J. Goh and I. Yu and C. Uribe Estrada and I. Pedraza and H. Castilla-Valdez and A. Sanchez-Hernandez and R. L. Fernandez and M. Ramirez-Garcia and E. Vazquez and M. A. Shah and N. Zaganidis and A. Radi and H. Hoorani and S. Muhammad and A. Ahmad and I. Asghar and W. A. Khan and J. Eysermans and F. Torres Da Silva De Araujo},
journal= {arXiv preprint arXiv:2302.02764},
year = {2023}
}