Machine Learning Applications for the Detection of Missing Radioactive Sources

Matthew Durbin, Austin Kuntz, Azaree Lintereur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The detection of missing radioactive material within a particular sample is advantageous for various applications of Materials Accountancy and Non-Destructive Assay. Examples of these applications include the monitoring of spent fuel pools and casks, as well as the inspection of fresh fuel assemblies. Currently employed methods for these processes include the use of passive or active gamma ray detection, where variation in detector responses are used to deduce if there is missing material and determine its expected location. This work investigates the feasibility of using machine learning algorithms for processing detection data in these scenarios to improve overall sensitivity. Preliminary simulated trials with a grid of nine 137Cs point sources and two NaI detectors show that a k-nearest neighbor algorithm can successfully predict the location of a missing source with 100% accuracy. Similar preliminary trials with up to two missing sources yielded an accuracy of 99%, suggesting that machine learning has promise for this application. These initial studies, as well as results with larger grids of sources, and trials with measurements taken in a laboratory setting are included.

Original languageEnglish (US)
Title of host publication2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141640
DOIs
StatePublished - Oct 2019
Event2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019 - Manchester, United Kingdom
Duration: Oct 26 2019Nov 2 2019

Publication series

Name2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019

Conference

Conference2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
CountryUnited Kingdom
CityManchester
Period10/26/1911/2/19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Radiology Nuclear Medicine and imaging
  • Nuclear and High Energy Physics

Fingerprint Dive into the research topics of 'Machine Learning Applications for the Detection of Missing Radioactive Sources'. Together they form a unique fingerprint.

  • Cite this

    Durbin, M., Kuntz, A., & Lintereur, A. (2019). Machine Learning Applications for the Detection of Missing Radioactive Sources. In 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019 [9059881] (2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSS/MIC42101.2019.9059881