Optimizing the Performance of Radionuclide Identification Software in the Hunt for Nuclear Security Threats

Technical Report · Thu Aug 18 00:00:00 EDT 2016

DOI:https://doi.org/10.2172/1305897· OSTI ID:1305897

Fotion, Katherine A. ^[1]

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1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

The Radionuclide Analysis Kit (RNAK), my team’s most recent nuclide identification software, is entering the testing phase. A question arises: will removing rare nuclides from the software’s library improve its overall performance? An affirmative response indicates fundamental errors in the software’s framework, while a negative response confirms the effectiveness of the software’s key machine learning algorithms. After thorough testing, I found that the performance of RNAK cannot be improved with the library choice effect, thus verifying the effectiveness of RNAK’s algorithms—multiple linear regression, Bayesian network using the Viterbi algorithm, and branch and bound search.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC52-07NA27344
• OSTI ID: 1305897
• Report Number(s): LLNL-TR-700884; TRN: US1601810
• Country of Publication: United States
• Language: English

Related Subjects

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION
97 MATHEMATICS AND COMPUTING
COMPUTER CODES
ALGORITHMS
PERFORMANCE
LIBRARIES
OPTIMIZATION
TESTING
ERRORS
RADIOISOTOPES
GAMMA SPECTRA
NUCLEAR MATERIALS MANAGEMENT
IDENTIFICATION SYSTEMS
FISSIONABLE MATERIALS