This thesis presents a measurement of the CP-properties of the Yukawa coupling between the Higgs boson and τ-lepton. The measurement uses the proton-proton collision data collected from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider. Totally 139 fb^-1 proton-proton collision data is collected at a center-of-mass energy of √s = 13 TeV. This study investigates the CP-properties with CP-sensitive observables defined by the visible decay products of τ-leptons. CP-violating interactions between the Higgs boson and τ-lepton are described by the CP-mixing angle Φ_τ. The expected value of Φ_τ according to the Standard Model is 0°±28° at 68% confidence level, and 0°_-70°^+75° at 95.5% confidence level obtained from a simulated dataset. The observed value of Φ_τ is 9° ± 16° at 68% c...[ Read more ]

This thesis presents a measurement of the CP-properties of the Yukawa coupling between the Higgs boson and τ-lepton. The measurement uses the proton-proton collision data collected from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider. Totally 139 fb^-1 proton-proton collision data is collected at a center-of-mass energy of √s = 13 TeV. This study investigates the CP-properties with CP-sensitive observables defined by the visible decay products of τ-leptons. CP-violating interactions between the Higgs boson and τ-lepton are described by the CP-mixing angle Φ_τ. The expected value of Φ_τ according to the Standard Model is 0°±28° at 68% confidence level, and 0°_-70°^+75° at 95.5% confidence level obtained from a simulated dataset. The observed value of Φ_τ is 9° ± 16° at 68% confidence level, and 9° ± 34° at 95.5% confidence level. The pure CP-odd hypothesis is disfavoured at 3.4 standard deviations. The observation is consistent with the Standard Model expectations.

This thesis also presents studies of pile-up jet tagging. The pile-up jet tagging algorithm is based on the K-Nearest Neighbor method. It is trained with the data collected from 2015 to 2018 with the ATLAS detector. A new pile-up jet tagging algorithm based on neural network is developed. It provides improvement to the background rejection at all working points.

In this thesis, a new CP-sensitive observable is developed using neural network approach. The trained observable excludes the pure CP-odd hypothesis at higher confidence level in a simplified sample compared with the previously designed observable. These studies will contribute to future measurements of the CP-properties.