Files in this item

FilesDescriptionFormat

application/pdf

application/pdf1994_graham.pdf (5MB)Restricted to U of Illinois
1994_grahamPDF

Description

Title:Quark and gluon jet discrimination by neutral networks
Author(s):Graham, Mary Ann
Doctoral Committee Chair(s):Jones, Lorella M.
Department / Program:Physics
Discipline:Physics
Degree:Ph.D.
Genre:Dissertation
Subject(s):quark
gluon
jet discrimination
high energy physics
Abstract:As the energy scales of high energy physics experiments increase, the amount of data which is available becomes difficult to manage. A method that can increase the signal to background ratio would be a clear advantage. The focus of the study reported here is on increasing the light quark jet signal to gluon jet background. We begin by demonstrating that there are characteristics common to quark jets and to gluon jets regardless of the interaction that produced them. The classification technique we use depends on the mass of the jet as well as center-of-mass energy of the hard subprocess that produces the jet. In addition, we present the quark-gluon jet separability results of an artificial neural network trained on three-jet e+ e- events at the Z0 mass, using a backpropagation algorithm. The inputs to the network are the longitudinal momenta of the leading hadrons in the jet. We tested the network with quark and gluon jets from both e+e--+ 3jets and pp-+ 2jets. Finally, we compare the performance of the artificial neural network with the results of making well chosen physical cuts.
Issue Date:1994
Genre:Dissertation / Thesis
Type:Text
Language:English
URI:http://hdl.handle.net/2142/18859
Rights Information:1994 Mary Ann Graham
Date Available in IDEALS:2011-04-21
Identifier in Online Catalog:3645100


This item appears in the following Collection(s)

Item Statistics