Uncertainty analysis of wavelet-based feature extraction for isotope identification on NaI gamma-ray spectra
Low-resolution isotope identifiers are widely deployed for nuclear security purposes, but these detectors currently demonstrate problems in making correct identifications in many typical usage scenarios. While there are many hardware alternatives and improvements that can be made, performance on existing low resolution isotope identifiers should be able to be improved by developing new identification algorithms. We have developed a wavelet-based peak extraction algorithm and an implementation of a Bayesian classifier for automated peak-based identification. The peak extraction algorithm has been extended to compute uncertainties in the peak area calculations. To build empirical joint probability distributions of the peak areas and uncertainties, a large set of spectra were simulated in MCNP6 and processed with the wavelet-based feature extraction algorithm. Kernel density estimation was then used to create a new component of the likelihood function in the Bayesian classifier. Identification performance is demonstrated on a variety of real low-resolution spectra, including Category I quantities of special nuclear material.
- Research Organization:
- Univ. of Michigan, Ann Arbor, MI (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
- Contributing Organization:
- University of Illinois
- Grant/Contract Number:
- NA0002534
- OSTI ID:
- 1367512
- Alternate ID(s):
- OSTI ID: 1798646
- Journal Information:
- IEEE Transactions on Nuclear Science, Vol. 64, Issue 7; ISSN 0018-9499
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
Cryogenic dark matter search (CDMS II): Application of neural networks and wavelets to event analysis
Sensitive and Specific Peak Detection for SELDI-TOF Mass Spectrometry Using a Wavelet/Neural-Network Based Approach