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在ATLAS 探测器中标定高横动量希格斯粒子和寻找新物理

Two of the most important targets of the Large Hadron Collider(LHC) is the precision measurement of the Standard Model and the search for Beyond Standard Model particles and interactions. In 2012, the ATLAS and CMS experiments completed the search for Standard Model particles with the discovery of t...

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Autor principal: Ding, Wei
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:http://cds.cern.ch/record/2773866
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author Ding, Wei
author_facet Ding, Wei
author_sort Ding, Wei
collection CERN
description Two of the most important targets of the Large Hadron Collider(LHC) is the precision measurement of the Standard Model and the search for Beyond Standard Model particles and interactions. In 2012, the ATLAS and CMS experiments completed the search for Standard Model particles with the discovery of the Higgs boson, then the precision measurement of the properties of the Higgs boson becomes really important. Boosted Higgs bosons decaying via the dominant 𝐻 → 𝑏𝑏̄ mode are an essential ingredient to a number of LHC physics signatures, ATLAS identifies these hadronic Higgs bosons by reconstructing a large-R jet with R=1, associating variable-radius subjets to the large-R jet, and then assigning a flavor discriminant to each of the subjets using standard flavor tagging. This thesis presents an algorithm to select boosted Higgs bosons that combines flavor discriminants from up to three subjets using a feed-forward neural network. In addition to the decorrelation from the large-R jet mass, a comparison of several algorithms that combine individual subjet discriminants show that the neural network-based algorithm is the most powerful, assessed on its ability to reject both boosted top quark jets and jets arising from QCD processes. In the meantime, for the Beyond Standard Model search, this thesis also presents a search for new resonances decaying into a pair of jets using the dataset of proton–proton collisions recorded at √𝑠=13TeV with the ATLAS detector at the LHC between 2015 and 2018, corresponding to an integrated luminosity of 139fb−1. The distribution of the invariant mass of the two leading jets is examined for local excesses above a data-derived estimate of the Standard Model background. In addition to an inclusive search, events with jets identified as containing 𝑏-hadrons are examined specifically, no significant excess of events above the smoothly falling background spectra is observed. The results are used to set cross-section upper limits at 95% confidence level on a range of new physics scenarios, model-independent limits on Gaussian-shaped signals are also reported. The analysis looking at jets containing 𝑏-hadrons benefits from improvements in the jet flavour identification at high transverse momentum, which increases its sensitivity relative to the previous analysis beyond that expected from the higher integrated luminosity.
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spelling cern-27738662021-06-25T19:35:57Zhttp://cds.cern.ch/record/2773866engDing, Wei在ATLAS 探测器中标定高横动量希格斯粒子和寻找新物理Particle Physics - ExperimentParticle Physics - ExperimentTwo of the most important targets of the Large Hadron Collider(LHC) is the precision measurement of the Standard Model and the search for Beyond Standard Model particles and interactions. In 2012, the ATLAS and CMS experiments completed the search for Standard Model particles with the discovery of the Higgs boson, then the precision measurement of the properties of the Higgs boson becomes really important. Boosted Higgs bosons decaying via the dominant 𝐻 → 𝑏𝑏̄ mode are an essential ingredient to a number of LHC physics signatures, ATLAS identifies these hadronic Higgs bosons by reconstructing a large-R jet with R=1, associating variable-radius subjets to the large-R jet, and then assigning a flavor discriminant to each of the subjets using standard flavor tagging. This thesis presents an algorithm to select boosted Higgs bosons that combines flavor discriminants from up to three subjets using a feed-forward neural network. In addition to the decorrelation from the large-R jet mass, a comparison of several algorithms that combine individual subjet discriminants show that the neural network-based algorithm is the most powerful, assessed on its ability to reject both boosted top quark jets and jets arising from QCD processes. In the meantime, for the Beyond Standard Model search, this thesis also presents a search for new resonances decaying into a pair of jets using the dataset of proton–proton collisions recorded at √𝑠=13TeV with the ATLAS detector at the LHC between 2015 and 2018, corresponding to an integrated luminosity of 139fb−1. The distribution of the invariant mass of the two leading jets is examined for local excesses above a data-derived estimate of the Standard Model background. In addition to an inclusive search, events with jets identified as containing 𝑏-hadrons are examined specifically, no significant excess of events above the smoothly falling background spectra is observed. The results are used to set cross-section upper limits at 95% confidence level on a range of new physics scenarios, model-independent limits on Gaussian-shaped signals are also reported. The analysis looking at jets containing 𝑏-hadrons benefits from improvements in the jet flavour identification at high transverse momentum, which increases its sensitivity relative to the previous analysis beyond that expected from the higher integrated luminosity.CERN-THESIS-2021-073oai:cds.cern.ch:27738662021-06-24T11:07:28Z
spellingShingle Particle Physics - Experiment
Particle Physics - Experiment
Ding, Wei
在ATLAS 探测器中标定高横动量希格斯粒子和寻找新物理
title 在ATLAS 探测器中标定高横动量希格斯粒子和寻找新物理
title_full 在ATLAS 探测器中标定高横动量希格斯粒子和寻找新物理
title_fullStr 在ATLAS 探测器中标定高横动量希格斯粒子和寻找新物理
title_full_unstemmed 在ATLAS 探测器中标定高横动量希格斯粒子和寻找新物理
title_short 在ATLAS 探测器中标定高横动量希格斯粒子和寻找新物理
title_sort 在atlas 探测器中标定高横动量希格斯粒子和寻找新物理
topic Particle Physics - Experiment
Particle Physics - Experiment
url http://cds.cern.ch/record/2773866
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