Publication Date
2019
Document Type
Dissertation/Thesis
First Advisor
Eads, Michael T.
Second Advisor
Bhat, Pushpalatha C.
Degree Name
M.S. (Master of Science)
Legacy Department
Department of Physics
Abstract
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.
Recommended Citation
Faia, Daniel Arthur Jr., "Study of Higgs production from H -> ZZ -> 4l Channel using Machine Learning Methods" (2019). Graduate Research Theses & Dissertations. 7019.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7019
Extent
72 pages
Language
eng
Publisher
Northern Illinois University
Rights Statement
In Copyright
Rights Statement 2
NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.
Media Type
Text
