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Topic

Optimization of event selection in search for doubly-charged Higgs bosons at ATLAS using machine learning techniques

Department of Physics and Astronomy

Date & location

• Friday, August 15, 2025
• 1:00 P.M.
• Clearihue Building, Room B017

Examining Committee

Supervisory Committee

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

External Examiner

• Dr. Alison Lister, Department of Physics and Astronomy, University of British Columbia

Chair of Oral Examination

• Dr. Jaya Prakash Champati, Department of Computer Science, UVic

Abstract

The analysis of proton-proton collision data recorded by ATLAS during Run 2 of the LHC identified an excess of data over the Standard Model prediction in both the 𝑊^±𝑍 and 𝑊^±𝑊^± vector boson scattering processes. These excesses could be attributed to resonances of the singly- and doubly-charged Higgs bosons, which are hypothesized by the Georgi-Machacek (GM) model. To investigate this excess and assess its compatibility with the GM model, a dedicated search is being performed for the 𝐻₅^± and 𝐻₅^±± bosons where they are produced by vector boson fusion and decay to 𝑊^±𝑍 and 𝑊^±𝑊^± respectively. In this thesis, the selection of the 𝐻₅^±± signal region is optimized by training a neural network to discriminate signal events from background events. The characteristics of the 𝐻₅^±± events vary significantly with mass, which leads to undesired behaviour when training a single network for a large mass range. A number of strategies are devised to address this problem; the best solution is to modify the weighting of different simulated masses during training. The neural network is used to define a new 𝑊^±𝑊^± signal region which has a greater sensitivity to the GM model compared to a cuts-based approach.