{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/18859"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/18859","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Quark and gluon jet discrimination by neutral networks","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Graham, Mary Ann"],"institution":null,"degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Jones, Lorella M."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-04-21T14:37:31Z","date_published":"2011-04-21T14:37:31Z","updated_at":"2026-07-22T22:25:11Z","subjects":["quark","gluon","jet discrimination","high energy physics"],"languages":["en"],"rights":["1994 Mary Ann Graham"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["3645100"],"render_values":[{"text":"3645100","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/18859","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jones, Lorella M."]},{"key":"dc:creator","label":"Author","values":["Graham, Mary Ann"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-04-21T14:37:31Z","10000-01-01","1994"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation / Thesis","text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Physics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["quark","gluon","jet discrimination","high energy physics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["1994 Mary Ann Graham"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["3645100","http://hdl.handle.net/2142/18859"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submitted by Carolyn Mead (cmead2@illinois.edu) on 2011-04-21T14:37:31Z No. of bitstreams: 1 1994_graham.pdf: 4914301 bytes, checksum: 2423b0842d6f287a78f38dfb4c5ceea6 (MD5)","Made available in DSpace on 2011-04-21T14:37:31Z (GMT). No. of bitstreams: 1 1994_graham.pdf: 4914301 bytes, checksum: 2423b0842d6f287a78f38dfb4c5ceea6 (MD5) Previous issue date: 1994","Restriction data tranferred 2014-07-01T11:12:09-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: Thesis","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Carolyn Mead (cmead2@illinois.edu) on 2011-04-21T14:37:31Z Item is restricted indefinitely.","Thesis","U of I Only"]},{"key":"dc:title","label":"Title","values":["Quark and gluon jet discrimination by neutral networks"]}]}],"canonical_facts":{"dc:contributor":["Jones, Lorella M."],"dc:creator":["Graham, Mary Ann"],"dc:date":["2011-04-21T14:37:31Z","10000-01-01","1994"],"dc:description":["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. 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