Publication Date


Document Type


First Advisor

Eads, Michael T.

Second Advisor

Bhat, Pushpalatha C.

Degree Name

M.S. (Master of Science)

Legacy Department

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.


72 pages




Northern Illinois University

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In Copyright

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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


Included in

Physics Commons