{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124591"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124591","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Evidential Deep Learning for uncertainty quantification in jet tagging deep neural network model","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Wang, Xiwei"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Kindratenko, Volodymyr","Neubauer, Mark"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["High Energy Physics","Evidential Deep Learning","Jet Tagging"],"languages":["en","eng"],"rights":["Copyright 2024 Xiwei Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124591","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kindratenko, Volodymyr","Neubauer, Mark"]},{"key":"dc:creator","label":"Author","values":["Wang, Xiwei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-30"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["High Energy Physics","Evidential Deep Learning","Jet Tagging"]}]},{"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 2024 Xiwei Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124591"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Xiwei Wang, accepted the attached license on 2024-04-28 at 15:42.","The student, Xiwei Wang, submitted this Thesis for approval on 2024-04-28 at 15:56.","This Thesis was approved for publication on 2024-04-30 at 15:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20682 on 2024-09-16 at 00:44:50","Evidential Deep Learning (EDL) is an uncertainty-aware deep learning method used in order to provide confidence about the input data. The learning based on the input data is treated as an evidence acquisition process and more evidence is interpreted as the increased predictive confidence. In this way, the model with EDL is able to quantify the epistemic uncertainty (uncertainty) of the input data and detect the anomaly in the data. In this study, we explore the integration of Evidential Deep Learning with the Particle Flow Identification Network (PFIN), a deep neural network model tailored for jet tagging in high-energy physics. We adapted EDL principles to enhance the PFIN model, enabling it not only to make predictions but also to estimate the confidence level of those predictions and detect possible anomaly. This adaptation involved developing an evidence layer within the DNN architecture, allowing the model to dynamically assess and quantify uncertainty by interpreting the input data's reliability and relevance. Our results show that the EDL-enhanced PFIN model significantly outperforms conventional models in uncertainty quantification without sacrificing predictive accuracy. This improvement is particularly notable in the detection of anomalous data, where the model's ability to quantify uncertainty provides a robust mechanism for identifying out-of-distribution data. Such capabilities are critical in high-energy physics experiments, where precise and reliable data interpretation can lead to groundbreaking discoveries."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Evidential Deep Learning for uncertainty quantification in jet tagging deep neural network model"]}]}],"canonical_facts":{"dc:contributor":["Kindratenko, Volodymyr","Neubauer, Mark"],"dc:creator":["Wang, Xiwei"],"dc:date":["2024-05","2024-04-30"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Xiwei Wang, accepted the attached license on 2024-04-28 at 15:42.","The student, Xiwei Wang, submitted this Thesis for approval on 2024-04-28 at 15:56.","This Thesis was approved for publication on 2024-04-30 at 15:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20682 on 2024-09-16 at 00:44:50","Evidential Deep Learning (EDL) is an uncertainty-aware deep learning method used in order to provide confidence about the input data. The learning based on the input data is treated as an evidence acquisition process and more evidence is interpreted as the increased predictive confidence. In this way, the model with EDL is able to quantify the epistemic uncertainty (uncertainty) of the input data and detect the anomaly in the data. In this study, we explore the integration of Evidential Deep Learning with the Particle Flow Identification Network (PFIN), a deep neural network model tailored for jet tagging in high-energy physics. We adapted EDL principles to enhance the PFIN model, enabling it not only to make predictions but also to estimate the confidence level of those predictions and detect possible anomaly. This adaptation involved developing an evidence layer within the DNN architecture, allowing the model to dynamically assess and quantify uncertainty by interpreting the input data's reliability and relevance. Our results show that the EDL-enhanced PFIN model significantly outperforms conventional models in uncertainty quantification without sacrificing predictive accuracy. This improvement is particularly notable in the detection of anomalous data, where the model's ability to quantify uncertainty provides a robust mechanism for identifying out-of-distribution data. Such capabilities are critical in high-energy physics experiments, where precise and reliable data interpretation can lead to groundbreaking discoveries."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124591"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Xiwei Wang"],"dc:subject":["High Energy Physics","Evidential Deep Learning","Jet Tagging"],"dc:title":["Evidential Deep Learning for uncertainty quantification in jet tagging deep neural network model"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}