{"paper":{"title":"CMS RPC Non-Physics Event Data Automation Ideology","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["physics.ins-det"],"primary_cat":"hep-ex","authors_text":"A. Ahmad, A. Aleksandrov, A. Braghieri, A. Cabrera, A. Dimitrov, A. Petrov, A. Radi, A. Samalan, A. Sanchez Hernandez, A. Santoro, B. Boghrati, B. Hong, B. Pavlov, C.A. Florez, C. Avila, C. Riccardi, C. Uribe Estrada, D.A. Barbosa Trujillo, D. De Jesus Damiao, D.L. Ramirez Guadarrama, D. Piccolo, D. Ramos, E. Alves Coelho, E. Asilar, E.M. Da Costa, E. Shumka, E. Vazquez, E. Zareian, F. Carnevali, F. Esfandi, F. Fienga, F. Loddo, F. Marujo da Silva, G.A. Alves, G. Grenier, G. Iaselli, G. Pugliese, G. Sultanov, H. Castilla-Valdez, H. Kou, H. Nogima, H.R. Hoorani, I. Bagaturia, I.B. Laktineh, I. Crotty, I. Lomidze, I. Pedraza, J.A. Reyes Vega, J. Eysermans, J. Goh, J.P. Pinheiro, J. Shin, J. Song, J. Zhao, K. Mota Amarilo, K. Skovpen, K.S. Lee, L. Benussi, L. Lista, L. Litov, L. Mirabito, L. Mundim, M. Abbrescia, M.A. Mahmoud, M.A. Shah, M. Barroso Ferreira Filho, M. Ebrahimi, M. Gouzevitch, M.I. Asghar, M. Mohammadi Najafabadi, M. Ramirez Garcia, M. Shopova, M. Thiel, M. Tytgat, N. De Filippis, N. Zaganidis, P. Cao, P. Iaydjiev, P. Montagna, P. Paolucci, P. Petkov, P. Salvini, P. Vitulo, Q. Hou, R.Aly, R. Gomes De Souza, R. Hadjiiska, R. Lopez-Fernandez, S. Bianco, S. Buontempo, S. Choi, S. Fonseca De Souza, S.J. Qian, S. Meola, S. Muhammad, T.J. Kim, V. Amoozegarp, W. Diao, Y. Assran, Y. Hosseini, Y. Lee, Y. Ryou, Z.-A. Liu, Z. Tsamalaidze","submitted_at":"2025-04-11T21:52:40Z","abstract_excerpt":"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 visu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.08991","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.08991/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}