Machine learning applications and observation of Higgs boson decays into a pair of bottom quarks with the ATLAS detector

Thesis

The discovery in 2012 of the Higgs boson at the Large Hadron Collider (LHC) represents a milestone for the Standard Model (SM) of particle physics. Most of the SM Higgs production and decay rates have been measured at the LHC with increased precision.

This thesis presents the analysis that led to the observation of the SM Higgs boson decay into pairs of bottom quarks. The analysis exploits the production of a Higgs boson associated with a vector boson whose signatures enable efficient triggering and powerful background reduction. The main strategy to maximise the signal sensitivity is based on a multivariate approach. The analysis is performed on a dataset corresponding to a luminosity of 79.8 fb^-1; collected by the ATLAS experiment during Run-2 at a centre-of-mass energy of 13 TeV. An excess of events over the expected background is found with an observed (expected) significance of 4.9 (4.3) standard deviation. A combination with results from other H → bb searches provides an observed (expected) significance of 5.4 (5.5). The corresponding ratio between the signal yield and the SM expectation is 1.01 ± 0.12 (stat.) ^{+ 0.16} _{- 0.15} (syst.).

The measurement of cross sections in exclusive regions of phase space of the VH production times the H → bb branching ratio is reported as well. These measurements are used to search for possible deviations from the SM with an effective field theory approach, based on anomalous couplings of the Higgs boson.

This thesis also describes a novel technique for the fast simulation of the forward calorimeter response, based on similarity search methods. The new simulation approach outperforms the default technique used by ATLAS.

Authors: Tosciri, C

• DOI: 10.5287/ora-kzaezdqmw
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: LHC; Similarity Search; Higgs boson; bottom quarks; ATLAS; Standard Model; Effective Field Theory; Machine Learning; Fast Simulation
• Subjects: Large Hadron Collider (France and Switzerland); Machine learning; Simulation methods; Particle Physics; Higgs bosons