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Title: DNDO Report: Developing a Photon Clutter Scaling Algorithm for Implementation into the NINESIM Tool

Technical Report ·
DOI:https://doi.org/10.2172/1467379· OSTI ID:1467379
 [1];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

The Nuclear Inspection Node Event SIMulator (NINESIM) is a tool that is capable of modeling a node that monitors passing traffic for radiological and nuclear materials. This task requires the consideration of a number of factors. One such factor is clutter, or the level of traffic passing through a node. Clutter might affect the performance of radiation detectors due to attenuation or scattering that particles undergo as they move through the material. This paper discusses the results from MCNP simulations that were performed to determine in what scenarios clutter had a significant impact on detector performance, where the receiver operating characteristic (ROC) curve was used as the metric to evaluate detector performance. Clutter was found to have an insignificant impact on neutron detection, therefore, clutter may be ignored when modeling neutron detectors. However, there were a number of scenarios where clutter was found to affect the performance of photon detectors; the amount of traffic was found to be directly proportional to the effectiveness of photon detectors. The results of this work were then used to develop a photon clutter scaling algorithm that will be implemented into NINESIM. The Domestic Nuclear Detection Office, and possibly other agencies such as the Defense Threat Reduction Agency, will benefit from this work by eventually obtaining a tool that can enhance their ability in countering the illicit movement of radiological and nuclear materials.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
U.S. Department of Homeland Security; USDOE
DOE Contract Number:
AC52-06NA25396
OSTI ID:
1467379
Report Number(s):
LA-UR-18-28231
Country of Publication:
United States
Language:
English

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