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Autores principales:	Dimitrov, A., Tytgat, M., Amarilo, K. Mota, Samalan, A., Skovpen, K., Alves, G. A., Coelho, E. Alves, da Silva, F. Marujo, Filho, M. Barroso Ferreira, Da Costa, E. M., Damiao, D. De Jesus, De Souza, S. Fonseca, De Souza, R. Gomes, Mundim, L., Nogima, H., Pinheiro, J. P., Santoro, A., Thiel, M., Aleksandrov, A., Hadjiiska, R., Iaydjiev, P., Shopova, M., Sultanov, G., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Cao, P., Diao, W., Hou, Q., Kou, H., Liu, Z. -A., Song, J., Zhao, J., Qian, S. J., Avila, C., Trujillo, D. A. Barbosa, Cabrera, A., Florez, C. A., Vega, J. A. Reyes, Aly, R., Radi, A., Assran, Y., Crotty, I., Mahmoud, M. A., Gouzevitch, M., Grenier, G., Laktineh, I. B., Mirabito, L., Bagaturia, I., Lomidze, I., Tsamalaidze, Z., Amoozegarp, V., Boghrati, B., Ebrahimi, M., Esfandi, F., Hosseini, Y., Najafabadi, M. Mohammadi, Zareian, E., Abbrescia, M., De Filippis, N., Iaselli, G., Loddo, F., Pugliese, G., Ramos, D., Benussi, L., Bianco, S., Meola, S., Piccolo, D., Buontempo, S., Carnevali, F., Fienga, F., Lista, L., Paolucci, P., Braghieri, A., Montagna, P., Riccardi, C., Salvini, P., Vitulo, P., Asilar, E., Kim, T. J., Ryou, Y., Choi, S., Hong, B., Lee, K. S., Goh, J., Shin, J., Lee, Y., Pedraza, I., Estrada, C. Uribe, Castilla-Valdez, H., Lopez-Fernandez, R., Hernandez, A. Sanchez, Garcia, M. Ramirez, Guadarrama, D. L. Ramirez, Shah, M. A., Vazquez, E., Zaganidis, N., Ahmad, A., Asghar, M. I., Hoorani, H. R., Muhammad, S., Eysermans, J.
Formato:	Preprint
Publicado:	2025
Materias:	High Energy Physics - Experiment Instrumentation and Detectors
Acceso en línea:	https://arxiv.org/abs/2504.08991
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

_version_	1866908316543746048
author	Dimitrov, A. Tytgat, M. Amarilo, K. Mota Samalan, A. Skovpen, K. Alves, G. A. Coelho, E. Alves da Silva, F. Marujo Filho, M. Barroso Ferreira Da Costa, E. M. Damiao, D. De Jesus De Souza, S. Fonseca De Souza, R. Gomes Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Thiel, M. Aleksandrov, A. Hadjiiska, R. Iaydjiev, P. Shopova, M. Sultanov, G. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Cao, P. Diao, W. Hou, Q. Kou, H. Liu, Z. -A. Song, J. Zhao, J. Qian, S. J. Avila, C. Trujillo, D. A. Barbosa Cabrera, A. Florez, C. A. Vega, J. A. Reyes Aly, R. Radi, A. Assran, Y. Crotty, I. Mahmoud, M. A. Gouzevitch, M. Grenier, G. Laktineh, I. B. Mirabito, L. Bagaturia, I. Lomidze, I. Tsamalaidze, Z. Amoozegarp, V. Boghrati, B. Ebrahimi, M. Esfandi, F. Hosseini, Y. Najafabadi, M. Mohammadi Zareian, E. Abbrescia, M. De Filippis, N. Iaselli, G. Loddo, F. Pugliese, G. Ramos, D. Benussi, L. Bianco, S. Meola, S. Piccolo, D. Buontempo, S. Carnevali, F. Fienga, F. Lista, L. Paolucci, P. Braghieri, A. Montagna, P. Riccardi, C. Salvini, P. Vitulo, P. Asilar, E. Kim, T. J. Ryou, Y. Choi, S. Hong, B. Lee, K. S. Goh, J. Shin, J. Lee, Y. Pedraza, I. Estrada, C. Uribe Castilla-Valdez, H. Lopez-Fernandez, R. Hernandez, A. Sanchez Garcia, M. Ramirez Guadarrama, D. L. Ramirez Shah, M. A. Vazquez, E. Zaganidis, N. Ahmad, A. Asghar, M. I. Hoorani, H. R. Muhammad, S. Eysermans, J.
author_facet	Dimitrov, A. Tytgat, M. Amarilo, K. Mota Samalan, A. Skovpen, K. Alves, G. A. Coelho, E. Alves da Silva, F. Marujo Filho, M. Barroso Ferreira Da Costa, E. M. Damiao, D. De Jesus De Souza, S. Fonseca De Souza, R. Gomes Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Thiel, M. Aleksandrov, A. Hadjiiska, R. Iaydjiev, P. Shopova, M. Sultanov, G. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Cao, P. Diao, W. Hou, Q. Kou, H. Liu, Z. -A. Song, J. Zhao, J. Qian, S. J. Avila, C. Trujillo, D. A. Barbosa Cabrera, A. Florez, C. A. Vega, J. A. Reyes Aly, R. Radi, A. Assran, Y. Crotty, I. Mahmoud, M. A. Gouzevitch, M. Grenier, G. Laktineh, I. B. Mirabito, L. Bagaturia, I. Lomidze, I. Tsamalaidze, Z. Amoozegarp, V. Boghrati, B. Ebrahimi, M. Esfandi, F. Hosseini, Y. Najafabadi, M. Mohammadi Zareian, E. Abbrescia, M. De Filippis, N. Iaselli, G. Loddo, F. Pugliese, G. Ramos, D. Benussi, L. Bianco, S. Meola, S. Piccolo, D. Buontempo, S. Carnevali, F. Fienga, F. Lista, L. Paolucci, P. Braghieri, A. Montagna, P. Riccardi, C. Salvini, P. Vitulo, P. Asilar, E. Kim, T. J. Ryou, Y. Choi, S. Hong, B. Lee, K. S. Goh, J. Shin, J. Lee, Y. Pedraza, I. Estrada, C. Uribe Castilla-Valdez, H. Lopez-Fernandez, R. Hernandez, A. Sanchez Garcia, M. Ramirez Guadarrama, D. L. Ramirez Shah, M. A. Vazquez, E. Zaganidis, N. Ahmad, A. Asghar, M. I. Hoorani, H. R. Muhammad, S. Eysermans, J.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2504_08991
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	CMS RPC Non-Physics Event Data Automation Ideology Dimitrov, A. Tytgat, M. Amarilo, K. Mota Samalan, A. Skovpen, K. Alves, G. A. Coelho, E. Alves da Silva, F. Marujo Filho, M. Barroso Ferreira Da Costa, E. M. Damiao, D. De Jesus De Souza, S. Fonseca De Souza, R. Gomes Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Thiel, M. Aleksandrov, A. Hadjiiska, R. Iaydjiev, P. Shopova, M. Sultanov, G. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Cao, P. Diao, W. Hou, Q. Kou, H. Liu, Z. -A. Song, J. Zhao, J. Qian, S. J. Avila, C. Trujillo, D. A. Barbosa Cabrera, A. Florez, C. A. Vega, J. A. Reyes Aly, R. Radi, A. Assran, Y. Crotty, I. Mahmoud, M. A. Gouzevitch, M. Grenier, G. Laktineh, I. B. Mirabito, L. Bagaturia, I. Lomidze, I. Tsamalaidze, Z. Amoozegarp, V. Boghrati, B. Ebrahimi, M. Esfandi, F. Hosseini, Y. Najafabadi, M. Mohammadi Zareian, E. Abbrescia, M. De Filippis, N. Iaselli, G. Loddo, F. Pugliese, G. Ramos, D. Benussi, L. Bianco, S. Meola, S. Piccolo, D. Buontempo, S. Carnevali, F. Fienga, F. Lista, L. Paolucci, P. Braghieri, A. Montagna, P. Riccardi, C. Salvini, P. Vitulo, P. Asilar, E. Kim, T. J. Ryou, Y. Choi, S. Hong, B. Lee, K. S. Goh, J. Shin, J. Lee, Y. Pedraza, I. Estrada, C. Uribe Castilla-Valdez, H. Lopez-Fernandez, R. Hernandez, A. Sanchez Garcia, M. Ramirez Guadarrama, D. L. Ramirez Shah, M. A. Vazquez, E. Zaganidis, N. Ahmad, A. Asghar, M. I. Hoorani, H. R. Muhammad, S. Eysermans, J. High Energy Physics - Experiment Instrumentation and Detectors 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.
title	CMS RPC Non-Physics Event Data Automation Ideology
topic	High Energy Physics - Experiment Instrumentation and Detectors
url	https://arxiv.org/abs/2504.08991

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