Generation of Synthetic Data for a Radiation Detection Algorithm Competition

Nicholson, Andrew D.; Peplow, Douglas E.; Ghawaly, James M.; Willis, Michael J.; Archer, Daniel E.

doi:10.1109/TNS.2020.3001754

Title: Generation of Synthetic Data for a Radiation Detection Algorithm Competition

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Abstract

This paper details the generation of synthetic radiation data using large-scale Monte Carlo transport models to evaluate radiation search detection algorithms. Modular 3-D Monte Carlo models spanning multiple city blocks were constructed, loosely based on downtown Knoxville, TN, containing buildings composed of multiple materials (brick, granite, and concrete), sidewalks, a four-lane road, side streets, parking lots, and grassy fields. Background and simulated source detector response calculations from these models were used to create synthetic list mode data sets for a 2"×4"×16" NaI(Tl) detector moving through a city street at a constant speed. For the background simulations, major isotopes were computed individually so that background composition and variability could be computed efficiently outside Monte Carlo. The source detector response included six simulated sources placed at 15 source locations. Detector response was developed to be periodic through the city street so that a detector path could begin at one end of the model and wrap around to the other. This framework allowed for the creation of diverse data sets, each with its own unique background and simulated source detector response. Synthetic data allows for high-quality labels, which are useful in developing data-driven radiation detection algorithms. This methodology was used to create syntheticmore »« less

Authors:

^[1];

^[1]; Ghawaly, James M. ^[2];

^[1];

^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Tennessee, Knoxville, TN (United States)

Publication Date:: Fri Jun 19 00:00:00 EDT 2020

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1651405

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Nuclear Science

Additional Journal Information:: Journal Volume: 67; Journal Issue: 8; Journal ID: ISSN 0018-9499

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Radiation Detectors; Monte Carlo Simulation; Security Applications; Environmental Radiation Effects; Radiation Environment Modeling; Algorithms

Citation Formats


                    Nicholson, Andrew D., Peplow, Douglas E., Ghawaly, James M., Willis, Michael J., and Archer, Daniel E. Generation of Synthetic Data for a Radiation Detection Algorithm Competition.  United States: N. p., 2020. 
Web.  doi:10.1109/TNS.2020.3001754.

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                    Nicholson, Andrew D., Peplow, Douglas E., Ghawaly, James M., Willis, Michael J., & Archer, Daniel E. Generation of Synthetic Data for a Radiation Detection Algorithm Competition.  United States.  https://doi.org/10.1109/TNS.2020.3001754

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                    Nicholson, Andrew D., Peplow, Douglas E., Ghawaly, James M., Willis, Michael J., and Archer, Daniel E. Fri .  
"Generation of Synthetic Data for a Radiation Detection Algorithm Competition".  United States.  https://doi.org/10.1109/TNS.2020.3001754.  https://www.osti.gov/servlets/purl/1651405.

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@article{osti_1651405,

  title        = {Generation of Synthetic Data for a Radiation Detection Algorithm Competition},

  author       = {Nicholson, Andrew D. and Peplow, Douglas E. and Ghawaly, James M. and Willis, Michael J. and Archer, Daniel E.},

  abstractNote = {This paper details the generation of synthetic radiation data using large-scale Monte Carlo transport models to evaluate radiation search detection algorithms. Modular 3-D Monte Carlo models spanning multiple city blocks were constructed, loosely based on downtown Knoxville, TN, containing buildings composed of multiple materials (brick, granite, and concrete), sidewalks, a four-lane road, side streets, parking lots, and grassy fields. Background and simulated source detector response calculations from these models were used to create synthetic list mode data sets for a 2"×4"×16" NaI(Tl) detector moving through a city street at a constant speed. For the background simulations, major isotopes were computed individually so that background composition and variability could be computed efficiently outside Monte Carlo. The source detector response included six simulated sources placed at 15 source locations. Detector response was developed to be periodic through the city street so that a detector path could begin at one end of the model and wrap around to the other. This framework allowed for the creation of diverse data sets, each with its own unique background and simulated source detector response. Synthetic data allows for high-quality labels, which are useful in developing data-driven radiation detection algorithms. This methodology was used to create synthetic data sets which were released as part of a public data competition to spur the development of new radiation detection algorithms for radiological search applications.},

  doi          = {10.1109/TNS.2020.3001754},

  journal      = {IEEE Transactions on Nuclear Science},

  number       = 8,

  volume       = 67,

  place        = {United States},

  year         = {Fri Jun 19 00:00:00 EDT 2020},

  month        = {Fri Jun 19 00:00:00 EDT 2020}

}

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