{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/13857"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/13857","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"Machine learning techniques for calorimeter cluster calibration of the CMS particle flow algorithm.","abstract":"The Electromagnetic Calorimeter (ECAL) and Hadronic Calorimeter (HCAL) are key components of the CMS detector. The ECAL is designed to measure the energies of electrons and photons, while the HCAL measures the energies of charged and neutral hadrons. The Particle Flow (PF) algorithm integrates information from various CMS sub-detectors to reconstruct and identify all particles produced in proton collisions. Photons and neural hadrons are reconstructed using PF element energy clusters. A proper calibration enhances particle identification and reduces the likelihood of misreconstructed energy excess. Machine learning techniques, such as Boosted Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. This thesis applies BDT to calibrate PF ECAL clusters, while GNN is tested for hadronic cluster calibration.","abstract_html":"The Electromagnetic Calorimeter (ECAL) and Hadronic Calorimeter (HCAL) are key components of the CMS detector. The ECAL is designed to measure the energies of electrons and photons, while the HCAL measures the energies of charged and neutral hadrons. The Particle Flow (PF) algorithm integrates information from various CMS sub-detectors to reconstruct and identify all particles produced in proton collisions. Photons and neural hadrons are reconstructed using PF element energy clusters. A proper calibration enhances particle identification and reduces the likelihood of misreconstructed energy excess. Machine learning techniques, such as Boosted Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. This thesis applies BDT to calibrate PF ECAL clusters, while GNN is tested for hadronic cluster calibration.","abstract_has_math":false,"creators":["Ghazwani, Noorah Asaad, 1992-"],"institution":"Baylor University.","degree_name":"M.S.","degree_level":"Masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Hatakeyama, Kenichi."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-24T01:08:04Z","subjects":["Machine learning.","Experimental particle physics.","Compact muon solenoid (CMS)","Calibration."],"languages":["en"],"rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2104/13857","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Hatakeyama, Kenichi."]},{"key":"dc:creator","label":"Author","values":["Ghazwani, Noorah Asaad, 1992-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-05T19:52:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Baylor University."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning.","Experimental particle physics.","Compact muon solenoid (CMS)","Calibration."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2104/13857"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The Electromagnetic Calorimeter (ECAL) and Hadronic Calorimeter (HCAL) are key components of the CMS detector. The ECAL is designed to measure the energies of electrons and photons, while the HCAL measures the energies of charged and neutral hadrons. The Particle Flow (PF) algorithm integrates information from various CMS sub-detectors to reconstruct and identify all particles produced in proton collisions. Photons and neural hadrons are reconstructed using PF element energy clusters. A proper calibration enhances particle identification and reduces the likelihood of misreconstructed energy excess. Machine learning techniques, such as Boosted Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. 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Photons and neural hadrons are reconstructed using PF element energy clusters. A proper calibration enhances particle identification and reduces the likelihood of misreconstructed energy excess. Machine learning techniques, such as Boosted Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. This thesis applies BDT to calibrate PF ECAL clusters, while GNN is tested for hadronic cluster calibration."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2104/13857"],"dc:language.iso":["en"],"dc:rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"dc:subject":["Machine learning.","Experimental particle physics.","Compact muon solenoid (CMS)","Calibration."],"dc:title":["Machine learning techniques for calorimeter cluster calibration of the CMS particle flow algorithm."],"dc:type":["Thesis"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S."],"thesis:institution_name":["Baylor University."]},"updated_at":"2026-07-24T01:08:04Z"}