{"id":{"repo_id":"catania","oai_identifier":"oai:www.iris.unict.it:20.500.11769/587048"},"canonical_url":"https://search.dev.ndltd.org/etd/catania/oai:www.iris.unict.it:20.500.11769/587048","repository":{"repo_id":"catania","name":"Università degli Studi di Catania","base_url":"https://www.iris.unict.it/oai/request"},"display":{"title":"Search for supersymmetric particles in opposite sign dilepton events with the CMS detector","abstract":"In this thesis a search for supersymmetric particles in the data collected by CMS at center of mass energy of 7 TeV is presented. The search is focused on the selection of events characterized by pairs of opposite sign electrons or muons. This particular exclusive signature (which we refer to as opposite sign dilepton signature) has a very good background rejection power and would permit systematic studies of the supersymmetric particles produced, once a sufficient amount of them is collected. The bulk of the analysis consists on the isolation of the events of new physics, through the performance of some selection cuts on properly chosen discriminating variables. Even if, after this step, most part of the standard model background is actually rejected, an estimate of the background events still surviving (mainly top pairs) is mandatory. The control of any residual background is here made using a particular data-driven method, involving many discriminating variables. Using this technique, we were able to set exclusion limits in the cMSSM plane at 95% of confidence level, using a Bayesian approach.","abstract_html":"In this thesis a search for supersymmetric particles in the data collected by CMS at center of mass energy of 7 TeV is presented. The search is focused on the selection of events characterized by pairs of opposite sign electrons or muons. This particular exclusive signature (which we refer to as opposite sign dilepton signature) has a very good background rejection power and would permit systematic studies of the supersymmetric particles produced, once a sufficient amount of them is collected. The bulk of the analysis consists on the isolation of the events of new physics, through the performance of some selection cuts on properly chosen discriminating variables. Even if, after this step, most part of the standard model background is actually rejected, an estimate of the background events still surviving (mainly top pairs) is mandatory. The control of any residual background is here made using a particular data-driven method, involving many discriminating variables. Using this technique, we were able to set exclusion limits in the cMSSM plane at 95% of confidence level, using a Bayesian approach.","abstract_has_math":false,"creators":["CAPPELLO, GIGI"],"institution":"Università degli studi di Catania","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["TRICOMI, Alessia Rita","RIGGI, Francesco"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-12-09","date_published":"2012-12-09","updated_at":"2026-07-24T01:35:17Z","subjects":["LHC, supersymmetry, leptons, CMS, Higgs"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess","license:PUBBLICO - Pubblico con Copyright","license uri:iris.PUB02"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.11769/587048","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Cappello, Gigi","TRICOMI, Alessia Rita","RIGGI, Francesco"]},{"key":"dc:creator","label":"Author","values":["CAPPELLO, GIGI"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012-12-09"]},{"key":"dc:publisher","label":"Institution","values":["Università degli studi di Catania","place:Catania"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["LHC, supersymmetry, leptons, CMS, Higgs"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:PUBBLICO - Pubblico con Copyright","license uri:iris.PUB02"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/20.500.11769/587048"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis a search for supersymmetric particles in the data collected by CMS at center of mass energy of 7 TeV is presented. The search is focused on the selection of events characterized by pairs of opposite sign electrons or muons. This particular exclusive signature (which we refer to as opposite sign dilepton signature) has a very good background rejection power and would permit systematic studies of the supersymmetric particles produced, once a sufficient amount of them is collected. The bulk of the analysis consists on the isolation of the events of new physics, through the performance of some selection cuts on properly chosen discriminating variables. Even if, after this step, most part of the standard model background is actually rejected, an estimate of the background events still surviving (mainly top pairs) is mandatory. The control of any residual background is here made using a particular data-driven method, involving many discriminating variables. 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The bulk of the analysis consists on the isolation of the events of new physics, through the performance of some selection cuts on properly chosen discriminating variables. Even if, after this step, most part of the standard model background is actually rejected, an estimate of the background events still surviving (mainly top pairs) is mandatory. The control of any residual background is here made using a particular data-driven method, involving many discriminating variables. 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