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dc.contributor.advisorMuheim, Franz
dc.contributor.advisorNeedham, Matthew
dc.contributor.authorGizdov, Konstantin
dc.date.accessioned2022-04-12T13:02:41Z
dc.date.available2022-04-12T13:02:41Z
dc.date.issued2022-04-11
dc.identifier.urihttps://hdl.handle.net/1842/38870
dc.identifier.urihttp://dx.doi.org/10.7488/era/2124
dc.description.abstractPrecise measurements of CP violation provide stringent tests of the Standard Model towards the search for signs of new physics. Using LHC proton-proton collision data, collected by the LHCb detector during 2015 and 2016 at the centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of 1.9 fb−1 , presented is the latest measurement of the CP -violating phase, ϕs , using B 0 s → J/ψϕ decays. The machine-learning-based data selection, data-driven corrections to simulated event samples, and the control of systematic effects using dedicated samples are discussed. The values ϕs = −0.083±0.041±0.006 rad, ∆Γs = 0.077± 0.008±0.003 ps−1 (i.e. the decay width difference between the light and the heavy mass eigenstates in the B 0 s system) and Γs −Γd = −0.0041±0.0024±0.0015 ps−1 (i.e. the difference of the average B 0 s and B 0 d meson decay widths) are obtained, yielding the World’s most precise determination of these quantities 1 . Furthermore, shown are the efforts and contributions towards the LHCb Upgrade: the quality assurance and testing of the LHCb RICH Upgrade components, and the redesign and upgrade of the fully online software trigger – LHCb HLT Upgrade. Regarding the former, an original implementation of a parallelised, robust and highly available automation system is introduced. In connection to the latter, a novel neural network architecture and optimisation methods are laid out, enabling complex machine learning to be performed in a low latency high-throughput environment. Those directly influence the future deployment of the experiment and its data collecting and analysis capabilities. Thus, they are essential for future more precise and stringent research.en
dc.contributor.sponsorScience and Technology Facilities Council (STFC)en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionSean Benson and Konstantin Gizdov. ‘NNDrone: a toolkit for the mass application of machine learning in High Energy Physics’. In: (2017). doi: 10.1016/j.cpc.2019.03.002. arXiv: 1712.09114 [hep-exen
dc.relation.hasversionKonstantin Gizdov. ‘Strategy and automation of the quality assurance testing of MaPMTs for the LHCb RICH upgrade’. In: Nucl. Instrum. Meth. A (2019). doi: 10.1016/j.nima.2019.05.046en
dc.relation.hasversionKonstantin Gizdov. ‘Measurement of CP Violation in B0 s → J/ψϕ Decays’. In: Proceedings, 16th Conference on Flavor Physics and CP Violation (FPCP 2018): Hyderabad, India, July 14-18, 2018. Ed. by Anjan Giri and Rukmani Mohanta. Vol. 234. 2019, pp. 245–251. doi: 10.1007/978- 3-030-29622-3en
dc.relation.hasversionKonstantin Gizdov. ‘First results from production testing of 64-channel MaPMT R13742 (1 in) and R13743 (2 in) for the LHCb RICH Upgrade’. July 2018. url: https://cds.cern.ch/record/2632422en
dc.relation.hasversionKonstantin Gizdov and Stefano Gallorini. PDQA Automation. 2018. url: https : / / gitlab . cern . ch / kgizdov / pdqa - automation (visited on 11/09/2018)en
dc.subjectCP violationen
dc.subjectkaonsen
dc.subjectLHCben
dc.subjectRing-imaging Čherenkov subdetectoren
dc.titleStudies of b Hadron decays to Charmonium, the LHCb upgrade and operationen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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