A robust method for detecting nuclear materials when the underlying model is inexact |
| |
| Affiliation: | 1. Depts. of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA;2. Depts. of Statistical and Actuarial Science, University of Iowa, Iowa City, IA 52242, USA;3. Depts. of Civil and Environmental Engineering, University of Iowa, Iowa City, IA 52242, USA;1. Department of Nuclear Engineering and Radiation Science, Missouri University of Science and Technology, 1201 N. State St., Rolla, MO, 65409, USA;2. Department of Nuclear Engineering, University of New Mexico, Albuquerque, NM 87131, USA;1. Department of Nuclear Science & Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Jiangsu Key Laboratory of Nuclear Energy Equipment Materials Engineering, Nanjing 210016, China;3. School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China |
| |
| Abstract: | This paper is concerned with the detection and identification of nuclides from weak and poorly resolved gamma-ray energy spectra when the underlying model is not known exactly. The algorithm proposed and tested here pairs an exciting and relatively new model selection algorithm with the method of total least squares. Gamma-ray counts are modeled as Poisson processes where the average part is taken to be the model and the difference between the observed gamma-ray counts and the model is considered random noise. Physics provides a template for the model, but we add uncertainty to this template to simulate real life conditions. Unlike most model selection algorithms whose utilities are demonstrated asymptotically, our method emphasizes selection when data is fixed and finite (after all, detector data is undoubtedly finite). Simulation examples provided here demonstrate the proposed algorithm performs well. |
| |
| Keywords: | Model selection Total least squares Nuclear material detection RIVAL LASSO |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
