{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/22352"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/22352","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 neural networks","abstract":"Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:57:02Z Item is restricted indefinitely.","abstract_html":"Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:57:02Z Item is restricted indefinitely.","abstract_has_math":false,"creators":["Graham, Mary Ann"],"institution":"University of Illinois at Urbana-Champaign","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":"2011-05-07T13:37:08Z","date_published":"2011-05-07T13:37:08Z","updated_at":"2026-07-22T22:25:19Z","subjects":["Physics, Elementary Particles and High Energy","Artificial Intelligence"],"languages":["eng"],"rights":["Copyright 1994 Graham, Mary Ann"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9416363","(UMI)AAI9416363"],"render_values":[{"text":"AAI9416363","href":null,"code":true},{"text":"(UMI)AAI9416363","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/22352","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":["2011-05-07T13:37:08Z","10000-01-01","1994"]},{"key":"dc:type","label":"Dc Type","values":["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."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Physics, Elementary Particles and High Energy","Artificial Intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1994 Graham, Mary Ann"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9416363","(UMI)AAI9416363","http://hdl.handle.net/2142/22352"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:57:02Z Item is restricted indefinitely.","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 $e\\sp+e\\sp-$ events at the $Z\\sp0$ 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 $e\\sp+ e\\sp-$ $\\to$ 3jets and pp $\\to$ 2jets.","Finally, we compare the performance of the artificial neural network with the results of making well chosen physical cuts.","Made available in DSpace on 2011-05-07T13:37:08Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9416363.pdf: 2742697 bytes, checksum: 2a769e1d70527d5941332d0122ee8de4 (MD5) Previous issue date: 1994","Restriction data tranferred 2014-07-01T11:26:42-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Quark and gluon jet discrimination by neural networks"]}]}],"canonical_facts":{"dc:contributor":["Jones, Lorella M."],"dc:creator":["Graham, Mary Ann"],"dc:date":["2011-05-07T13:37:08Z","10000-01-01","1994"],"dc:description":["Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:57:02Z Item is restricted indefinitely.","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 $e\\sp+e\\sp-$ events at the $Z\\sp0$ 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 $e\\sp+ e\\sp-$ $\\to$ 3jets and pp $\\to$ 2jets.","Finally, we compare the performance of the artificial neural network with the results of making well chosen physical cuts.","Made available in DSpace on 2011-05-07T13:37:08Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9416363.pdf: 2742697 bytes, checksum: 2a769e1d70527d5941332d0122ee8de4 (MD5) Previous issue date: 1994","Restriction data tranferred 2014-07-01T11:26:42-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"],"dc:identifier":["AAI9416363","(UMI)AAI9416363","http://hdl.handle.net/2142/22352"],"dc:language":["eng"],"dc:rights":["Copyright 1994 Graham, Mary Ann"],"dc:subject":["Physics, Elementary Particles and High Energy","Artificial Intelligence"],"dc:title":["Quark and gluon jet discrimination by neural networks"],"dc:type":["text"],"thesis:degree_discipline":["Physics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:19Z"}