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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01w6634693h
Title: Search for a Pseudoscalar Higgs Boson
Authors: DeZoort, Gage
Advisors: Marlow, Dan
Contributors: Physics Department
Subjects: Particle physics
Artificial intelligence
Issue Date: 2023
Publisher: Princeton, NJ : Princeton University
Abstract: Beyond the Standard Model (BSM) theories are motivated by well-known deficiencies of the Standard Model (SM). One of the simplest extensions to the SM is the addition of a second Higgs doublet, specifying a broad class of models called two-Higgs-doublet models (2HDMs). An immediate motivation for 2HDMs is the Higgs sector of the minimal supersymmetric SM, which is a Type-II 2HDM. The 2HDM Higgs sector is characterized by five physical states: two neutral scalars, one pseudoscalar, and two charged Higgs bosons. The observation of any such additional Higgs boson would constitute smoking gun evidence for BSM theories with extended Higgs sectors. This thesis describes a search for a heavy pseudoscalar Higgs boson (A) belonging to the Type-II 2HDM using 138 inverse femtobarns of data collected at the CERN Compact Muon Solenoid (CMS) during Run 2. Two production modes, gluon-gluon fusion and associated production with a bottom quark-antiquark pair, are considered, and A decays to Zh with llττ final states are targeted. At the time of this writing, the full results of the analysis are not public; kinematic distributions and expected limits computed using simulation are shown throughout. The final results will be available pending the completion of an internal CMS review process. An additional chapter is included describing technical work on charged particle tracking via graph neural networks (GNNs). This work is motivated by the problem of tracking in the high-pileup conditions expected at the High Luminosity Large Hadron Collider. Several approaches are discussed, including small GNNs implemented on FPGAs and full GNN tracking pipelines based on object condensation, a contrastive learning technique that has shown promise in calorimetry applications.
URI: http://arks.princeton.edu/ark:/88435/dsp01w6634693h
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Physics

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