Optimization of event selection in search for doubly-charged Higgs bosons at ATLAS using machine learning techniques
| dc.contributor.author | Scott, Adrienne | |
| dc.contributor.supervisor | Lefebvre, Michel | |
| dc.date.accessioned | 2025-08-27T21:53:27Z | |
| dc.date.available | 2025-08-27T21:53:27Z | |
| dc.date.issued | 2025 | |
| dc.degree.department | Department of Physics and Astronomy | |
| dc.degree.level | Master of Science MSc | |
| dc.description.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-charged (H±) and doubly-charged (H±±) 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 H± and H±± 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 H±± signal region is optimized by training a neural network to discriminate signal events from background events. The characteristics of the H±± 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. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/22667 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | Particle physics | |
| dc.subject | ATLAS | |
| dc.subject | LHC | |
| dc.title | Optimization of event selection in search for doubly-charged Higgs bosons at ATLAS using machine learning techniques | |
| dc.type | Thesis |