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.
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- Resource Type:
- Technical Report
- Research Org:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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- Country of Publication:
- United States
- 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; 97 MATHEMATICS AND COMPUTING
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