Identification of Additional Jets in the t(t)over-barb(b)over-bar Events by Using Deep Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Jieun | - |
dc.contributor.author | Kim, Tae Jeong | - |
dc.contributor.author | Lim, Jongwon | - |
dc.contributor.author | Park, Jiwon | - |
dc.contributor.author | Ryou, Yeonsu | - |
dc.contributor.author | Song, Juhee | - |
dc.contributor.author | Yun, Soohyun | - |
dc.date.accessioned | 2022-07-07T09:33:03Z | - |
dc.date.available | 2022-07-07T09:33:03Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 0374-4884 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/144277 | - |
dc.description.abstract | In the top quark pair production in association with the Higgs boson decaying to a b quark pair (tt¯H(bb¯)), the final state has an irreducible nonresonant background from the production of a top quark pair in association with a b quark pair (tt¯bb¯). Therefore, understanding of the tt¯bb¯ process precisely in particular differential cross-section as functions of the properties of the additional b jets not from the top quark decay is essential for improving the sensitivity of a search for the tt¯H(bb¯)process. The two additional b jets can be identified by using various approaches. In this paper, the performances are compared quantitatively in the lepton+jets decay channel in terms of the matching efficiency of assigning two additional b jets as a figure of merit. We showed that a matching efficiency of around 40% could be achieved using a deep neural network method. In the events with at least 4 b jets, this performance is 8% better than that achieved using minimum △R(b,b¯) method. This is consistent with the boosted decision tree method within its statistical uncertainty. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | KOREAN PHYSICAL SOC | - |
dc.title | Identification of Additional Jets in the t(t)over-barb(b)over-bar Events by Using Deep Neural Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Tae Jeong | - |
dc.identifier.doi | 10.3938/jkps.77.1100 | - |
dc.identifier.scopusid | 2-s2.0-85096290961 | - |
dc.identifier.wosid | 000590984800005 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE KOREAN PHYSICAL SOCIETY, v.77, no.12, pp.1100 - 1106 | - |
dc.relation.isPartOf | JOURNAL OF THE KOREAN PHYSICAL SOCIETY | - |
dc.citation.title | JOURNAL OF THE KOREAN PHYSICAL SOCIETY | - |
dc.citation.volume | 77 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 1100 | - |
dc.citation.endPage | 1106 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Physics, Multidisciplinary | - |
dc.subject.keywordPlus | PP COLLISIONS | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordAuthor | Top quark | - |
dc.subject.keywordAuthor | Bottom quark | - |
dc.subject.keywordAuthor | Deep neural network | - |
dc.identifier.url | https://link.springer.com/article/10.3938%2Fjkps.77.1100 | - |
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