CMS RPC non-physics event data automation ideology

This paper presents a streamlined framework for real-time processing and analysis of condition data from the CMS experiment Resistive Plate Chambers (RPC). Leveraging data streaming, it uncovers correlations between RPC performance metrics, like currents and rates, and LHC luminosity or environmental conditions. The Java-based framework automates data handling and predictive modeling, integrating extensive datasets into synchronized, query-optimized tables. By segmenting LHC operations and analyzing larger virtual detector objects, the automation enhances monitoring precision, accelerates visualization, and provides predictive insights, revolutionizing RPC performance evaluation and future behavior modeling.

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Publication Details

Journal
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment
Published
2025-04-14
DOI
https://doi.org/10.1016/j.nima.2025.170506
Primary Topic
Particle Detector Development and Performance
Type
article
Field-Weighted Citation Impact
0.00
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article

CMS RPC non-physics event data automation ideology

A. Santoro, I. Lomidze, Neal Patrick Smith, E. Asilar et al.
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment
Particle Detector Development and Performance
article

CMS RPC non-physics event data automation ideology

A. Santoro, I. Lomidze, Neal Patrick Smith, E. Asilar, Andrés Cabrera, P. Cao, H. Kou, C. Uribe Estrada, J. Eysermans, Jie Song, H. Castilla-Valdez, E. Zareian, K. Skovpen, I. Crotty, A. Sánchez-Hernández, E. Shumka, A. Samalan, C. Ávila, P. Iaydjiev, H. R. Hoorani, F. Marujo da Silva, M. Shopova, E. Coelho, Yeonsu Ryou, A. Braghieri, A. Dimitrov, D. Ramos, A. Petrov, K. Mota Amarilo, J. Shin, Kyong Sei Lee, L. Mundim, F. Loddo, G. Iaselli, Q. Hou, J. Zhao, P. Salvini, L. Litov, R. Hadjiiska, T. J. Kim, I. Pedraza, V. Amoozegar, B. Pavlov, E. M. Da Costa, G. Sultanov, Yasaman Hosseini, G. A. Alves, J.P. Pinheiro, D.L. Ramirez Guadarrama, P. Vitulo, C. Riccardi, Y. Assran, Ashfaq Ahmad, D. Piccolo, S. Bianco, M. Ebrahimi, P. Paolucci, Fabiola Vazquez Valencia, F. Fienga, A. Radi, S. J. Qian, G. Grenier, C. Florez, M. Gouzevitch, J. A. Reyes Vega, N. Zaganidis, I. Bagaturia, Zhenan Liu, F. Carnevali, J. Goh, G. Pugliese, Diego Andres Barbosa Trujillo, Z. Tsamalaidze, P. Montagna, R. Aly, M. Tytgat, S. Choi, B. Hong, D. De Jesus Damião, M. Thiel, Mehar Ali Shah, B. Boghrati, S. Muhammad, S. Meola, M. Abbrescia, R. Gomes De Souza, S. Fonseca De Souza, Y. Lee, R. López-Fernández, H. Nogima, M. Ramirez-García, M. A. Mahmoud, S. Buontempo, M. Mohammadi Najafabadi, L. Mirabito, M. I. Asghar, W. Diao, M. Barroso Ferreira Filho, L. Benussi, P. Petkov, L. Lista, N. De Filippis, А. Александров, F. Esfandi
article en

Abstract

This paper presents a streamlined framework for real-time processing and analysis of condition data from the CMS experiment Resistive Plate Chambers (RPC). Leveraging data streaming, it uncovers correlations between RPC performance metrics, like currents and rates, and LHC luminosity or environmental conditions. The Java-based framework automates data handling and predictive modeling, integrating extensive datasets into synchronized, query-optimized tables. By segmenting LHC operations and analyzing larger virtual detector objects, the automation enhances monitoring precision, accelerates visualization, and provides predictive insights, revolutionizing RPC performance evaluation and future behavior modeling.

Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated EquipmentVol. 1076
Vrije Universiteit Brussel (BE), Bulgarian Academy of Sciences (BG), Ghent University (BE), Universidade do Estado do Rio de Janeiro (BR), Institute for Nuclear Research and Nuclear Energy (BG), Centro Brasileiro de Pesquisas Físicas (BR), Sofia University "St. Kliment Ohridski" (BG)
Openalex Percentile: Top 96%
Particle Detector Development and Performance
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