Speaker
Description
An exploration into machine-learning based optimisation techniques applied to the extraction of the top quark Yukawa coupling ($Y_{t}$) from top quark pair production in the dilepton final state, using proton collisions recorded by the ATLAS experiment in LHC run 2 and 3 is presented. Electroweak corrections for the exchange of the Higgs boson between the top quark and the top anti-quark for different values of $Y_{t}$ result in changes to the kinematic distributions of the top quark and top anti-quark system close to the $t\bar{t}$ production energy threshold. The aim of this analysis is to use the kinematic distributions of the $t\bar{t}$ events in the dilepton final state to constrain the value for $Y_{t}$. The observables that are most sensitive to $Y_{t}$ are the invariant mass of the $t\bar{t}$ system, $m_{t\bar{t}}$, and the difference in rapidity, $\Delta y_{t\bar{t}}$. As the decay products of the $t\bar{t}$ system involve neutrinos in the dilepton final state, reconstructing the top quark and top anti-quark introduces resolution effects and additional systematic uncertainties. The proxy observables $m_{l\bar{l}b\bar{b}}$ (invariant mass of the $ l\bar{l}b\bar{b}$ system) and $\Delta y_{lblb}$ (the difference in rapidity between the two $lb$ pairs) are used to mitigate this effect. In this talk, I will discuss the current state of the project, as well as future plans.
Level for award;(Hons, MSc, PhD, N/A)?
MSc
| Apply to be considered for a student ; award (Yes / No)? | No |
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