{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115319"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115319","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Search for Neutrinoless Double Beta Decay with EXO-200 and the Application of Deep Learning to Detector Simulation","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_has_math":false,"creators":["Li, Shaolei"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Yang, Liang","Perdekamp, Matthias Grosse","Draper, Patrick I","MacDougall, Gregory"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["Physics"],"languages":["en","eng"],"rights":["Copyright 2022 Shaolei Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115319","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yang, Liang","Perdekamp, Matthias Grosse","Draper, Patrick I","MacDougall, Gregory"]},{"key":"dc:creator","label":"Author","values":["Li, Shaolei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2021-12-22"]},{"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":["Physics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Shaolei Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115319"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Shaolei Li, accepted the attached license on 2021-12-17 at 18:37.","The student, Shaolei Li, submitted this Dissertation for approval on 2021-12-17 at 18:48.","This Dissertation was approved for publication on 2021-12-22 at 13:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17465 on 2022-11-11 at 13:03:56","The EXO-200 detector is designed to search for the neutrinoless double beta decay (0νββ) of 136Xe. Such a decay, if observed, would demonstrate the Majorana nature of neutrino; set the mass scale of the neutrino sector; and demonstrate lepton number non-conservation. The EXO- 200 detector and its successor, nEXO, use liquid Xenon time projection technology to perform the search. One important performance parameter of the detector is its energy resolution. In the first part of this work, we review the analysis work to improve the energy resolution and the status of the 0νββ search. This includes a description of the advanced analysis techniques used to maximize the energy resolution with improved charge channels, calibration ,and more precise Light-Maps. The second part of this work presents the current state of deep learning efforts towards fast simulations of the scintillation signals using Wasserstein Generative Adversarial Network (GAN) algorithms."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Search for Neutrinoless Double Beta Decay with EXO-200 and the Application of Deep Learning to Detector Simulation"]}]}],"canonical_facts":{"dc:contributor":["Yang, Liang","Perdekamp, Matthias Grosse","Draper, Patrick I","MacDougall, Gregory"],"dc:creator":["Li, Shaolei"],"dc:date":["2022-05","2021-12-22"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Shaolei Li, accepted the attached license on 2021-12-17 at 18:37.","The student, Shaolei Li, submitted this Dissertation for approval on 2021-12-17 at 18:48.","This Dissertation was approved for publication on 2021-12-22 at 13:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17465 on 2022-11-11 at 13:03:56","The EXO-200 detector is designed to search for the neutrinoless double beta decay (0νββ) of 136Xe. Such a decay, if observed, would demonstrate the Majorana nature of neutrino; set the mass scale of the neutrino sector; and demonstrate lepton number non-conservation. The EXO- 200 detector and its successor, nEXO, use liquid Xenon time projection technology to perform the search. One important performance parameter of the detector is its energy resolution. In the first part of this work, we review the analysis work to improve the energy resolution and the status of the 0νββ search. This includes a description of the advanced analysis techniques used to maximize the energy resolution with improved charge channels, calibration ,and more precise Light-Maps. The second part of this work presents the current state of deep learning efforts towards fast simulations of the scintillation signals using Wasserstein Generative Adversarial Network (GAN) algorithms."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115319"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Shaolei Li"],"dc:subject":["Physics"],"dc:title":["Search for Neutrinoless Double Beta Decay with EXO-200 and the Application of Deep Learning to Detector Simulation"],"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":"2026-07-22T22:24:54Z"}