Eads, Michael T.
Bhat, Pushpalatha C.
M.S. (Master of Science)
Department of Physics
In this thesis I will show how machine learning methods can improve on physics analysis in the H -> ZZ -> 4l channel. In particular we will explore how these methods can be used to classify Vector Boson Fusion (VBF) processes in the presence of more dominant Higgs production processes. The aim is to improve the ability to discriminate VBF Higgs boson production relative to other Higgs boson production modes. Since VBF has two quark jets in the final state, it is useful to discriminate between quark and gluon jets. We compare the effectiveness of quark gluon discrimination with machine learning with that of Matrix Element Likelihood Method. We find that quark gluon discrimination with Convolutional Neural Networks show promise.
Faia, Daniel Arthur Jr., "Study of Higgs production from H -> ZZ -> 4l Channel using Machine Learning Methods" (2019). Graduate Research Theses & Dissertations. 7019.
Northern Illinois University
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