{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108545"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108545","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Methodology for anomalous source detection in sparse gamma-ray spectra","abstract":"The student, Gregory Romanchek, accepted the attached license on 2020-07-23 at 18:42.","abstract_html":"The student, Gregory Romanchek, accepted the attached license on 2020-07-23 at 18:42.","abstract_has_math":false,"creators":["Romanchek, Gregory R."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Nuclear, Plasma, Radiolgc Engr","degree_department":null,"school":null,"contributors":["Abbaszadeh, Shiva","Di Fulvio, Angela"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T21:00:11Z","date_published":"2020-10-07T21:00:11Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Anomaly Detection","Radiation","Statistical Learning","Machine Learning","Gaussian Process","Linear Innovation Sequences"],"languages":["en"],"rights":["Copyright 2020 Gregory R Romanchek"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108545","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abbaszadeh, Shiva","Di Fulvio, Angela"]},{"key":"dc:creator","label":"Author","values":["Romanchek, Gregory R."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-10-07T21:00:11Z","2020-07-24","2020-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nuclear, Plasma, Radiolgc 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":["Anomaly Detection","Radiation","Statistical Learning","Machine Learning","Gaussian Process","Linear Innovation Sequences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Gregory R Romanchek"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108545"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Gregory Romanchek, accepted the attached license on 2020-07-23 at 18:42.","The student, Gregory Romanchek, submitted this Thesis for approval on 2020-07-23 at 18:50.","The dangers of rogue nuclear material remain a top concern despite increased attention and strides in computational protocols. Single, mobile detector methodologies for localizing sources via autonomous surveying have become popular with the maturation of the machine learning (ML) and statistical learning (SL) fields as well as increased access to drone (quad-copter) technology. These options, however, face task-inherent impediments which either degrade the quality of collected gamma-ray spectra or necessitate high-quality information on source and background spectrum compositions. Some such hurdles include: limited dwell periods, fluctuating and/or unknown background, weak source signal due to large distance and/or small/shielded activity, and the low sensitivity of mobile detectors. As such, collected gamma-ray spectra are sparse, containing many zero-count energy channels, and contain relatively large background presence. This combination of factors, as well as the natural variance in second-to-second count rates, leads to low-quality information for making navigational decisions. In this thesis, an SL algorithm is presented for extracting source count estimations from time-series, sparse gamma-ray spectra with no prior training required. A Gaussian process with a linear innovation sequences procedure is used to efficiently update ongoing spectral estimates with real-time training and hyperparameters defined by detector characteristics. Being free of prior training and assumptions allows such an algorithm to be used in a wide variety of sparse-data settings whereas a trained solution would have very narrow applications. We have evaluated the effectiveness of this approach for anomaly detection using background spectra dataset collected with a Kromek D3S and simulated source spectra. Results of anomaly detection testing with a source count rate at half that of the background displays an area under the ROC curve of 0.9. Further, deployment with an ML guided navigation scheme shows, after an anomaly is detected, estimated gross source counts and true gross source counts have an average correlation of 0.998, whereas estimated gross background counts and true gross background counts have an average correlation of 0.876.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-10-02 without embargo terms","This Thesis was approved for publication on 2020-07-24 at 13:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15740 on 2020-10-02 at 15:15:28","Made available in DSpace on 2020-10-07T21:00:11Z (GMT). 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Single, mobile detector methodologies for localizing sources via autonomous surveying have become popular with the maturation of the machine learning (ML) and statistical learning (SL) fields as well as increased access to drone (quad-copter) technology. These options, however, face task-inherent impediments which either degrade the quality of collected gamma-ray spectra or necessitate high-quality information on source and background spectrum compositions. Some such hurdles include: limited dwell periods, fluctuating and/or unknown background, weak source signal due to large distance and/or small/shielded activity, and the low sensitivity of mobile detectors. As such, collected gamma-ray spectra are sparse, containing many zero-count energy channels, and contain relatively large background presence. This combination of factors, as well as the natural variance in second-to-second count rates, leads to low-quality information for making navigational decisions. In this thesis, an SL algorithm is presented for extracting source count estimations from time-series, sparse gamma-ray spectra with no prior training required. A Gaussian process with a linear innovation sequences procedure is used to efficiently update ongoing spectral estimates with real-time training and hyperparameters defined by detector characteristics. Being free of prior training and assumptions allows such an algorithm to be used in a wide variety of sparse-data settings whereas a trained solution would have very narrow applications. We have evaluated the effectiveness of this approach for anomaly detection using background spectra dataset collected with a Kromek D3S and simulated source spectra. Results of anomaly detection testing with a source count rate at half that of the background displays an area under the ROC curve of 0.9. Further, deployment with an ML guided navigation scheme shows, after an anomaly is detected, estimated gross source counts and true gross source counts have an average correlation of 0.998, whereas estimated gross background counts and true gross background counts have an average correlation of 0.876.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-10-02 without embargo terms","This Thesis was approved for publication on 2020-07-24 at 13:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15740 on 2020-10-02 at 15:15:28","Made available in DSpace on 2020-10-07T21:00:11Z (GMT). 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