Implementation of machine learning algorithms for detecting missing radioactive material

被引：2

作者：

Durbin, Matthew ^{[1
]}

Lintereur, Azaree ^{[1
]}

机构：

[1] Penn State Univ, Ken & Mary Alice Lindquist Dept Nucl Engn, 205 Hallowell Bldg, University Pk, PA 16801 USA

来源：

JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY | 2020年 / 324卷 / 03期

关键词：

Nuclear safeguards; Machine learning; Material unaccounted for; Gamma-ray detection;

D O I：

10.1007/s10967-020-07188-4

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

The detection of missing radioactive material is an important capability for safeguards measurements. Gamma ray signatures provide sample information, but interpretation is complicated by measurement environments. To determine if machine learning is a viable analysis option, three algorithms are applied to gamma ray detection data to assess their success at identifying missing sources. Preliminary results demonstrate that these algorithms can predict the number and location of missing sources on simple models of spent fuel assemblies. In addition to simulated experiments, a study to investigate if the algorithms can be trained with simulated data and tested on measured data is presented.

引用

页码：1455 / 1461

页数：7

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