Abstract
A nuclear forensics technique, based on the maximum likelihood method, for the attribution of reactor type, fuel burnup, and time since irradiation (TSI) of separated pure plutonium (Pu) samples was previously developed at Texas A&M University. The method utilized measured values of ten intra-elemental isotope ratios in the Pu sample and a large database consisting of the values for these ratios as a function of the three attributes: reactor type, fuel burnup, and TSI. However, this method failed for Pu samples with mixed attributes. Hence, a new technique based on machine learning methods was developed that matched the capabilities of the previous maximum likelihood method for pure Pu samples. This new methodology used support vector machines for reactor-type discrimination and Gaussian process regression for fuel burnup quantification. The TSI was calculated analytically using the predicted reactor type and fuel burnup. This new method holds great potential for the attribution of mixed Pu samples.
| Original language | English |
|---|---|
| Pages (from-to) | 811-823 |
| Number of pages | 13 |
| Journal | Nuclear Science and Engineering |
| Volume | 196 |
| Issue number | 7 |
| DOIs | |
| State | Published - 2022 |
| Externally published | Yes |
Funding
This work was funded by the Consortium for Monitoring, Technology, and Verification under U.S. Department of Energy National Nuclear Security Administration award number DE-NA0003920. The authors greatly appreciate the English language editing and proofreading assistance provided by Kelley Holle Ragusa, of the Texas A&M Center for Nuclear Security Science and Policy Initiatives. The authors would also like to thank the insights provided by Brendan Donohoe at Sandia National Laboratories in producing the results contained in Table II. This work was supported by the Office of Defense Nuclear Nonproliferation [DE-NA0003920]. This work was funded by the Consortium for Monitoring, Technology, and Verification under U.S. Department of Energy National Nuclear Security Administration award number DE-NA0003920. The authors greatly appreciate the English language editing and proofreading assistance provided by Kelley Holle Ragusa, of the Texas A&M Center for Nuclear Security Science and Policy Initiatives. The authors would also like to thank the insights provided by Brendan Donohoe at Sandia National Laboratories in producing the results contained in . This work was supported by the Office of Defense Nuclear Nonproliferation [DE-NA0003920].
| Funders | Funder number |
|---|---|
| U.S. Department of Energy National Nuclear Security Administration | |
| National Nuclear Security Administration | DE-NA0003920 |
| Office of Defense Nuclear Nonproliferation |
Keywords
- Nuclear forensics
- fuel burnup
- machine learning
- reactor type
- separated plutonium
- time since irradiation