Séminaires et colloques

[postdoc seminar] Search for new interactions in the top quark sector

par Ana Peixoto (Laboratoire de Physique Subatomique et de Cosmologie de Grenoble (LPSC))

Europe/Paris
Grand Amphi (LPSC)

Grand Amphi

LPSC

Description

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Meeting ID: 832 360 7482, passcode: 661136
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Abstract: The top quark is the heaviest elementary particle discovered so far and the study of its unique properties provides not only an essential test of the Standard Model of Particle Physics, but also an important window into physics beyond it. My PhD thesis presented a search for Flavour Changing Neutral Currents processes through the top quark interactions with a neutral Z boson and a light quark, that can be an up or charm quark. The analysis used a 139 fb−1 dataset of proton-proton collisions collected at a centre-of-mass of √s = 13 TeV with the ATLAS detector, at the Large Hadron Collider between 2015 and 2018. The analysis focused on the final state composed by three leptons, resulting from the leptonic decays of the top quark and the Z boson. In order to improve the sensitivity of the search, methods based on multivariate techniques were developed and applied to dedicated signal regions to discriminate tZq events against background events, which are dominated by top quark pair production in association with a Z boson as well as diboson production. All the analysis' regions were combined in a statistical model using a profile likelihood fit to constrain the background predictions and reduce the systematic uncertainties. Since no evidence for a signal was found, upper limits on the cross-section and branching ratio of these processes at 95% confidence level were evaluated, being the most stringent limits up to date. Through a phenomenological study, the search for a new pseudo-scalar particle S has also been investigated focusing on several leptonic final states, in collaboration with theorists from the University of Granada (Spain). My current efforts as a post-doc at the LPSC ATLAS group focus on the search for dark QCD and the improvement of the calibration of the jets using Machine Learning techniques.