Special Nuclear Material Analysis Using Temporal Gamma-Ray Spectroscopy and Machine Learning Methods Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/f7623h05t

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Following thermal neutron induced fission, supervised machine learning and temporal gamma-ray spectroscopy methods were used to identify differences in the delayed gamma-ray spectra of Pu-239 and U-235. The temporal gamma-ray spectroscopy method takes advantage of the time-dependent decay of fission products. Employing Spearman's rank-order correlation coefficient and without prior knowledge of peak locations or their associated energies, temporal behavior patterns characteristic of radioactive decay were identified within the complex fission product gamma-ray spectra below 3 MeV of Pu-239 and U-235. Individual rho (rho [subscript s]) values and their respective differences (Δ rho[subscript s]) were used as a part of feature selection to identify channels with significant attributes used as "fingerprints'' to create profiles for Pu-239 and U-235 which may be used for the identification of temporal behavior in an unknown sample. This method may be combined with additional machine learning techniques for future quantification of fissile material with the potential for similar accuracy and precision to previous temporal gamma-ray spectrometry methods.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Subject
Rights Statement
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Submitted by Jessica Curtis (curtisj@oregonstate.edu) on 2016-06-30T23:31:15Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) CurtisJessicaR2016.pdf: 3206738 bytes, checksum: bad8c4e370e60e1985ce09ac4e216447 (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2016-07-05T23:24:30Z (GMT) No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) CurtisJessicaR2016.pdf: 3206738 bytes, checksum: bad8c4e370e60e1985ce09ac4e216447 (MD5)
  • description.provenance : Made available in DSpace on 2016-07-05T23:24:30Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) CurtisJessicaR2016.pdf: 3206738 bytes, checksum: bad8c4e370e60e1985ce09ac4e216447 (MD5) Previous issue date: 2016-06-09
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2016-07-05T21:23:25Z (GMT) No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) CurtisJessicaR2016.pdf: 3206738 bytes, checksum: bad8c4e370e60e1985ce09ac4e216447 (MD5)

Relationships

In Administrative Set:
Last modified: 08/22/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items