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arxiv: 2504.08991 · v1 · pith:PBVMQKE6 · submitted 2025-04-11 · hep-ex · physics.ins-det

CMS RPC Non-Physics Event Data Automation Ideology

A. Dimitrov , M. Tytgat , K. Mota Amarilo , A. Samalan , K. Skovpen , G.A. Alves , E. Alves Coelho , F. Marujo da Silva
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M. Barroso Ferreira Filho E.M. Da Costa D. De Jesus Damiao S. Fonseca De Souza R. Gomes De Souza L. Mundim H. Nogima J.P. Pinheiro A. Santoro M. Thiel A. Aleksandrov R. Hadjiiska P. Iaydjiev M. Shopova G. Sultanov L. Litov B. Pavlov P. Petkov A. Petrov E. Shumka P. Cao W. Diao Q. Hou H. Kou Z.-A. Liu J. Song J. Zhao S.J. Qian C. Avila D.A. Barbosa Trujillo A. Cabrera C.A. Florez J.A. Reyes Vega R.Aly A. Radi Y. Assran I. Crotty M.A. Mahmoud M. Gouzevitch G. Grenier I.B. Laktineh L. Mirabito I. Bagaturia I. Lomidze Z. Tsamalaidze V. Amoozegarp B. Boghrati M. Ebrahimi F. Esfandi Y. Hosseini M. Mohammadi Najafabadi E. Zareian M. Abbrescia N. De Filippis G. Iaselli F. Loddo G. Pugliese D. Ramos L. Benussi S. Bianco S. Meola D. Piccolo S. Buontempo F. Carnevali F. Fienga L. Lista P. Paolucci A. Braghieri P. Montagna C. Riccardi P. Salvini P. Vitulo E. Asilar T.J. Kim Y. Ryou S. Choi B. Hong K.S. Lee J. Goh J. Shin Y. Lee I. Pedraza C. Uribe Estrada H. Castilla-Valdez R. Lopez-Fernandez A. Sanchez Hernandez M. Ramirez Garcia D.L. Ramirez Guadarrama M.A. Shah E. Vazquez N. Zaganidis A. Ahmad M.I. Asghar H.R. Hoorani S. Muhammad J. Eysermans
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classification hep-ex physics.ins-det
keywords dataautomationframeworkmodelingperformancepredictiveacceleratesanalysis
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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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