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Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

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Data for training and testing radiation detection algorithms in an urban environment.

James M Ghawaly¹, Andrew D Nicholson², Douglas E Peplow²

¹Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA. ghawalyjm@ornl.gov.

Scientific Data

|October 6, 2020

Summary

A new dataset of simulated radiation detection data aids the development of algorithms for locating nuclear materials in cities. This resource supports national security by improving detection and identification capabilities.

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Area of Science:

Nuclear security
Radiation detection and identification
Algorithm development

Background:

Detecting illicit nuclear materials in urban settings is critical for national security.
Current methods rely on trained personnel with radiation detection devices and algorithms.
There is a need for advanced algorithms for detection, identification, and localization.

Purpose of the Study:

To encourage the development of novel algorithms for nuclear material detection, identification, and localization.
To provide a realistic dataset for testing and validating these algorithms.
To enhance capabilities for securing urban environments against nuclear threats.

Main Methods:

Developed a dataset using Monte Carlo simulations of radiation detection data.
Simulated a 2 in. × 4 in. × 16 in. NaI(Tl) scintillation detector in a virtual urban environment (Knoxville, Tennessee).
Verified the dataset's methodology with experimental data from Fort Indiantown Gap National Guard facility.

Main Results:

Created and released a public dataset via a Topcoder competition.
The dataset includes realistic signals from special nuclear materials, industrial, and medical sources.
The simulation methodology is validated against real-world experimental data.

Conclusions:

The developed dataset provides a valuable resource for advancing nuclear material detection and localization algorithms.
This initiative supports the development of more effective tools for national security in urban areas.
The dataset facilitates testing algorithms in dynamic, real-world background conditions.