UNIVERSITY OF BUCHAREST
FACULTY OF PHYSICS

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2026-06-11 23:58
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Conference: Bucharest University Faculty of Physics 2026 Meeting


Section: High-Energy Physics


Title:
Study of the Higgs boson in the H → ZZ∗ → 4ℓ channel with the ATLAS experiment


Authors:
Alexandru MOROȘANU (1), Călin ALEXA (2), Roxana ZUS (1)


Affiliation:
1) Facultatea de Fizica, Universitatea din București

2) IFIN-HH RO, ATLAS


E-mail
morosanu.alexandru@gmail.com


Keywords:
HEPP, Higgs, CERN, Machine Learning


Abstract:
The subject of this presentation has as a central topic the study of the Higgs mechanism and field by experimental procedures at the ATLAS experiment at CERN. The Standard Model is briefly described, along with the electroweak theory’s prior problem of the origin of the fields’ mass (direct introduction in the SM Lagrangian violated gauge invariance), resulting in the postulation of the Higgs boson. The analysis process that led to its discovery was replicated according to ATLAS OpenData notebooks, to which modifications were added to form a cohesive and relevant analysis. The general idea of this project is to offer a detailed description of the phenomenological process of studying elementary particles, applied to a relevant research topic and touching on as many useful aspects as possible. The ATLAS experiment is presented briefly, along with the properties of the decay channel chosen for analysis, the Higgs boson decay to two Z bosons. The steps followed show a general procedure of selection and preparation of methods and parameters, especially the distinction between signal and background. First, a classic cut-and-count method is applied. The properties of signal and background are observed and as a first proper measurement, the 4-lepton invariant mass distribution is calculated, along with the significance of the Higgs boson mass resonance peak at 125 GeV. The secondary objective of this project follows a comparison between data processing methods in HEPP: cut-and-count and machine learning. Two algorithms are fitted and applied, namely boosted decision trees and neural networks, to obtain a better classification of the data. After a thorough characterization of the architectures of the models, a cross-comparison is performed, considering their working methods, efficiencies, and obtained significance.


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Acknowledgement:
I want to thank all the teachers and students that are part of the ATLAS Colaboration in Romania, who've helped me established the direction and overall quality of the project, while also giving me the liberty to experiment.