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A new approach for CMS RPC current monitoring using Machine Learning techniques

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dc.contributor.authorSamalan, A.-
dc.contributor.authorTytgat, M.-
dc.contributor.authorZaganidis, N.-
dc.contributor.authorAlves, G. A.-
dc.contributor.authorMarujo, F.-
dc.contributor.authorDa Silva De Araujo, F. Torres-
dc.contributor.authorDa Costa, E. M.-
dc.contributor.authorDe Jesus Damiao, D.-
dc.contributor.authorNogima, H.-
dc.contributor.authorSantoro, A.-
dc.contributor.authorFonseca De Souza, S.-
dc.contributor.authorAleksandrov, A.-
dc.contributor.authorHadjiiska, R.-
dc.contributor.authorIaydjiev, P.-
dc.contributor.authorRodozov, M.-
dc.contributor.authorShopova, M.-
dc.contributor.authorSultanov, G.-
dc.contributor.authorBonchev, M.-
dc.contributor.authorDimitrov, A.-
dc.contributor.authorLitov, L.-
dc.contributor.authorPavlov, B.-
dc.contributor.authorPetkov, P.-
dc.contributor.authorPetrov, A.-
dc.contributor.authorQian, S. J.-
dc.contributor.authorBernal, C.-
dc.contributor.authorCabrera, A.-
dc.contributor.authorFraga, J.-
dc.contributor.authorSarkar, A.-
dc.contributor.authorElsayed, S.-
dc.contributor.authorAssran, Y.-
dc.contributor.authorEl Sawy, M.-
dc.contributor.authorMahmoud, M. A.-
dc.contributor.authorMohammed, Y.-
dc.contributor.authorCombaret, C.-
dc.contributor.authorGouzevitch, M.-
dc.contributor.authorGrenier, G.-
dc.contributor.authorLaktineh, I-
dc.contributor.authorMirabito, L.-
dc.contributor.authorShchablo, K.-
dc.contributor.authorBagaturia, I-
dc.contributor.authorLomidze, D.-
dc.contributor.authorLomidze, I-
dc.contributor.authorBhatnagar, V-
dc.contributor.authorGupta, R.-
dc.contributor.authorKumari, P.-
dc.contributor.authorSingh, J.-
dc.contributor.authorAmoozegar, V-
dc.contributor.authorBoghrati, B.-
dc.contributor.authorEbraimi, M.-
dc.contributor.authorGhasemi, R.-
dc.contributor.authorNajafabadi, M. Mohammadi-
dc.contributor.authorZareian, E.-
dc.contributor.authorAbbrescia, M.-
dc.contributor.authorAly, R.-
dc.contributor.authorElmetenawee, W.-
dc.contributor.authorFilippis, N.-
dc.contributor.authorGelmi, A.-
dc.contributor.authorIaselli, G.-
dc.contributor.authorLeszki, S.-
dc.contributor.authorLoddo, F.-
dc.contributor.authorMargjeka, I-
dc.contributor.authorPugliese, G.-
dc.contributor.authorRamos, D.-
dc.contributor.authorBenussi, L.-
dc.contributor.authorBianco, S.-
dc.contributor.authorPiccolo, D.-
dc.contributor.authorBuontempo, S.-
dc.contributor.authorDi Crescenzo, A.-
dc.contributor.authorFienga, F.-
dc.contributor.authorDe Lellis, G.-
dc.contributor.authorLista, L.-
dc.contributor.authorMeola, S.-
dc.contributor.authorPaolucci, P.-
dc.contributor.authorBraghieri, A.-
dc.contributor.authorSalvini, P.-
dc.contributor.authorMontagna, P.-
dc.contributor.authorRiccardi, C.-
dc.contributor.authorVitulo, P.-
dc.contributor.authorFrancois, B.-
dc.contributor.authorKim, T. J.-
dc.contributor.authorPark, J.-
dc.contributor.authorChoi, S. Y.-
dc.contributor.authorHong, B.-
dc.contributor.authorLee, K. S.-
dc.contributor.authorGoh, J.-
dc.contributor.authorLee, H.-
dc.contributor.authorEysermans, J.-
dc.contributor.authorUribe Estrada, C.-
dc.contributor.authorPedraza, I-
dc.contributor.authorCastilla-Valdez, H.-
dc.contributor.authorSanchez-Hernandez, A.-
dc.contributor.authorMondragon Herrera, C. A.-
dc.contributor.authorPerez Navarro, D. A.-
dc.contributor.authorAyala Sanchez, G. A.-
dc.contributor.authorCarrillo, S.-
dc.contributor.authorVazquez, E.-
dc.contributor.authorRadi, A.-
dc.contributor.authorAhmad, A.-
dc.contributor.authorAsghar, I-
dc.contributor.authorHoorani, H.-
dc.contributor.authorMuhammad, S.-
dc.contributor.authorShah, M. A.-
dc.contributor.authorCrottya, I-
dc.date.accessioned2022-07-07T14:37:22Z-
dc.date.available2022-07-07T14:37:22Z-
dc.date.created2021-05-11-
dc.date.issued2020-10-
dc.identifier.issn1748-0221-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145066-
dc.description.abstractThe CMS experiment has 1054 RPCs in its muon system. Monitoring their currents is the first essential step towards maintaining the stability of the CMS RPC detector performance. The current depends on several parameters such as applied voltage, luminosity, environmental conditions, etc. Knowing the influence of these parameters on the RPC current is essential for the correct interpretation of its instabilities as they can be caused either by changes in external conditions or by malfunctioning of the detector in the ideal case. We propose a Machine Learning(ML) based approach to be used for monitoring the CMS RPC currents. The approach is crucial for the development of an automated monitoring system capable of warning for possible hardware problems at a very early stage, which will contribute further to the stable operation of the CMS RPC detector.-
dc.language영어-
dc.language.isoen-
dc.publisherIOP PUBLISHING LTD-
dc.titleA new approach for CMS RPC current monitoring using Machine Learning techniques-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, T. J.-
dc.identifier.doi10.1088/1748-0221/15/10/C10009-
dc.identifier.scopusid2-s2.0-85092630108-
dc.identifier.wosid000577277600009-
dc.identifier.bibliographicCitationJOURNAL OF INSTRUMENTATION, v.15, no.10, pp.1 - 10-
dc.relation.isPartOfJOURNAL OF INSTRUMENTATION-
dc.citation.titleJOURNAL OF INSTRUMENTATION-
dc.citation.volume15-
dc.citation.number10-
dc.citation.startPage1-
dc.citation.endPage10-
dc.type.rimsART-
dc.type.docTypeArticle; Proceedings Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaInstruments & Instrumentation-
dc.relation.journalWebOfScienceCategoryInstruments & Instrumentation-
dc.subject.keywordPlusMachine learning-
dc.subject.keywordPlusTuring machines-
dc.subject.keywordPlusMonitoring-
dc.subject.keywordPlusApplied voltages-
dc.subject.keywordPlusAutomated monitoring systems-
dc.subject.keywordPlusCurrent monitoring-
dc.subject.keywordPlusDetector performance-
dc.subject.keywordPlusEnvironmental conditions-
dc.subject.keywordPlusExternal conditions-
dc.subject.keywordPlusHardware problems-
dc.subject.keywordPlusMachine learning techniques-
dc.subject.keywordAuthorLarge detector-systems performance-
dc.subject.keywordAuthorResistive-plate chambers-
dc.identifier.urlhttps://iopscience.iop.org/article/10.1088/1748-0221/15/10/C10009-
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