{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110404"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110404","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A search for supersymmetry with the ATLAS detector, and the use of machine learning techniques for object classification in high energy physics","abstract":"We conduct a search for supersymmetry using data from the ATLAS detector at CERN, in a region with 2 leptons, 2 jets, and large MET. We also demonstrate the development of various machine learning techniques to enhance similar physics searches in the future, including the use of neural nets on calorimeter data for particle-type classification, particle energy regression, and shower generation.","abstract_html":"We conduct a search for supersymmetry using data from the ATLAS detector at CERN, in a region with 2 leptons, 2 jets, and large MET. We also demonstrate the development of various machine learning techniques to enhance similar physics searches in the future, including the use of neural nets on calorimeter data for particle-type classification, particle energy regression, and shower generation.","abstract_has_math":false,"creators":["Zhang, Matt"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Hooberman, Ben","Neubauer, Mark","Cooper, Lance","Shelton, Jessie"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:10:28Z","date_published":"2021-09-17T01:10:28Z","updated_at":"2026-07-22T22:24:50Z","subjects":["particle physics","high energy physics","HEP","machine learning","ATLAS","CERN","data science","supersymmetry"],"languages":["en"],"rights":["Copyright 2021 Matt Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110404","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hooberman, Ben","Neubauer, Mark","Cooper, Lance","Shelton, Jessie"]},{"key":"dc:creator","label":"Author","values":["Zhang, Matt"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:10:28Z","2021-01-12","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["particle physics","high energy physics","HEP","machine learning","ATLAS","CERN","data science","supersymmetry"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Matt Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110404"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We conduct a search for supersymmetry using data from the ATLAS detector at CERN, in a region with 2 leptons, 2 jets, and large MET. We also demonstrate the development of various machine learning techniques to enhance similar physics searches in the future, including the use of neural nets on calorimeter data for particle-type classification, particle energy regression, and shower generation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Matt Zhang, accepted the attached license on 2020-12-31 at 00:52.","The student, Matt Zhang, submitted this Dissertation for approval on 2020-12-31 at 00:58.","This Dissertation was approved for publication on 2021-01-12 at 09:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16138 on 2021-09-16 at 16:39:09","Made available in DSpace on 2021-09-17T01:10:28Z (GMT). No. of bitstreams: 3 ZHANG-DISSERTATION-2021.pdf: 19504448 bytes, checksum: 053428b66da340b07ab8f1b163c67aba (MD5) LICENSE.txt: 4207 bytes, checksum: 5732aff0a83e1555c703fdc9bd55e7ee (MD5) PROQUEST_LICENSE.txt: 4553 bytes, checksum: 6302f9abfed86c4b7f73c22a2393903e (MD5) Previous issue date: 2021-01-12"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A search for supersymmetry with the ATLAS detector, and the use of machine learning techniques for object classification in high energy physics"]}]}],"canonical_facts":{"dc:contributor":["Hooberman, Ben","Neubauer, Mark","Cooper, Lance","Shelton, Jessie"],"dc:creator":["Zhang, Matt"],"dc:date":["2021-09-17T01:10:28Z","2021-01-12","2021-05"],"dc:description":["We conduct a search for supersymmetry using data from the ATLAS detector at CERN, in a region with 2 leptons, 2 jets, and large MET. We also demonstrate the development of various machine learning techniques to enhance similar physics searches in the future, including the use of neural nets on calorimeter data for particle-type classification, particle energy regression, and shower generation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Matt Zhang, accepted the attached license on 2020-12-31 at 00:52.","The student, Matt Zhang, submitted this Dissertation for approval on 2020-12-31 at 00:58.","This Dissertation was approved for publication on 2021-01-12 at 09:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16138 on 2021-09-16 at 16:39:09","Made available in DSpace on 2021-09-17T01:10:28Z (GMT). No. of bitstreams: 3 ZHANG-DISSERTATION-2021.pdf: 19504448 bytes, checksum: 053428b66da340b07ab8f1b163c67aba (MD5) LICENSE.txt: 4207 bytes, checksum: 5732aff0a83e1555c703fdc9bd55e7ee (MD5) PROQUEST_LICENSE.txt: 4553 bytes, checksum: 6302f9abfed86c4b7f73c22a2393903e (MD5) Previous issue date: 2021-01-12"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/110404"],"dc:language":["en"],"dc:rights":["Copyright 2021 Matt Zhang"],"dc:subject":["particle physics","high energy physics","HEP","machine learning","ATLAS","CERN","data science","supersymmetry"],"dc:title":["A search for supersymmetry with the ATLAS detector, and the use of machine learning techniques for object classification in high energy physics"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Physics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:50Z"}