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

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.
Authors:
 [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
OSTI Identifier:
1305897
Report Number(s):
LLNL-TR--700884
TRN: US1601810
DOE Contract Number:
AC52-07NA27344
Resource Type:
Technical Report
Research Org:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
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