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Topic

Derivation of Electron Charge Misidentification Scale Factors with Run 2 and Run 3 Data at the ATLAS Experiment

Department of Physics and Astronomy

Date & location

• Monday, August 18, 2025
• 2:00 P.M.
• Clearihue Building, Room B017

Examining Committee

Supervisory Committee

• Dr. Justin Albert, Department of Physics and Astronomy, ßÉßɱ¬ÁÏ (Supervisor)
• Dr. Heather Russell, Department of Physics and Astronomy, UVic (Member)

External Examiner

• Dr. Katherine Pachal, Research Scientist, TRIUMF

Chair of Oral Examination

• Dr. Evanthia Baboula, Department of Art History and Visual Studies, UVic

Abstract

Electron charge misidentification constitutes a significant background in analyses involving same-sign electron pairs, such as searches for electroweak production of same-sign 𝑊^±𝑊^± bosons. An estimation of this background is essential for improving signal sensitivity in such processes. This thesis presents the derivation of charge misidentification scale factors using a Deep Neural Network (DNN) based electron identification (ID) in the ATLAS experiment, utilizing proton–proton collision data produced at √𝑠=13 TeV and √𝑠=13.6 TeV. A data-driven method based on the 𝑍→𝑒⁺𝑒⁻ process is employed to estimate charge flip probabilities in data and Monte Carlo simulation, across different kinematic bins of transverse momentum and pseudorapidity. The DNN-based ID algorithm offers improved discrimination power compared to traditional likelihood-based methods, particularly in complex detector regions. The derived scale factors correct for mismodelling of charge flip rates in Monte Carlo simulations and are parametrized in both one-dimensional and two-dimensional schemes. Closure tests are performed to validate the robustness of the scale factors and their applicability across various physics analyses.