Search for H->cc in the VH production mode and flavour-taggin development
Search for H->cc in the VH production mode and flavour-taggin development
批准号:
2880979
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
Vid Homsak will utilize the data collected by the ATLAS detector at the Large Hadron Collider (LHC) in Geneva, Switzerland, for in-depth exploration of the Higgs Boson. Although the Higgs Boson was discovered in 2012, our knowledge is limited to its interactions with the W and Z bosons, as well as quarks and charged leptons of the third generation. The study of the Higgs boson's interaction with a second-generation quark, such as the charm quark, is crucial for substantiating the validity of the Higgs mechanism in explaining the three generations of matter particles, which differ in mass from the lightest (first generation) to the heaviest (third generation). The detection of Higgs decay into charm quarks (H->cc) presents significant challenges, as its branching fraction is merely 3%, and charm quark production at the LHC is predominantly obscured by background processes. This endeavour will not only advance our understanding of the Higgs->cc coupling but also hold the potential to unveil new physics beyond the standard model.In addition to his work on the Higgs Boson, Vid will make significant contributions to the enhancement of heavy-flavor jet tagging. He will be an active participant in the collaborative efforts led by the ATLAS Common Physics group, aimed at refining the performance of this critical tool. Vid's involvement extends to deriving light jet scale factors for GN2 in release 24, which will involve the application of an innovative approach to complement the existing "flip" tagger. This approach includes the implementation of a new method into the extrapolation framework for the precise estimation of uncertainties, particularly in the context of b-/c-tagging mismodeling originating from long-lived particles, conversions, and hadronic interactions.Vid will take on the responsibility of generating datasets with a bottom-up approach, employing them to extract systematic variations from the data, and subsequently propagating these variations to the flavor tagging Calibration Data Interface.
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