DOE OSTI · 3427309
RADAI: A Large-Scale Realistic Dataset for Radiation Detection Algorithm Development
Abstract
Open, realistic datasets are essential for developing and benchmarking radiation detection algorithms, yet they remain scarce. The Radiological Anomaly Detection and Identification (RADAI) project was develop to create datasets that meet the training and testing needs for sophisticated radiation detection algorithms. The RADAI dataset is a large-scale synthetic resource that integrates high-fidelity Monte Carlo simulations with realistic urban scenarios to capture both background variability and source signatures. RADAI models construction-material NORM, people and vehicles, urban clutter, and dynamic environmental effects such as cosmic-ray and rain-induced transients, and they provide list-mode detector data with motion and response modeling suitable for algorithm training and evaluation. The RADAI project resulted in three publicly-released complementary datasets together with an online scoring portal for standardized performance assessment and an open software toolkit that supports data access, augmentation, model development, and evaluation. These resources enable reproducible comparisons across methods and promote rigorous studies at the scale required by contemporary machine learning. By grounding algorithm development in realistic, well-documented conditions, RADAI supports progress toward more robust detection, identification, and localization in complex urban environments.
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Ghawaly, James M. [Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA, USA] (ORCID:0000000188268500), Archer, Daniel E. [Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA], Nicholson, Andrew D. [Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA] (ORCID:0000000153035424), Peplow, Douglas E. [Nuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA] (ORCID:0000000292081914), Prins, Nicholas J. [Nuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA], Joshi, Tenzing H. Y. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA] (ORCID:0000000308467871), Bandstra, Mark S. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA] (ORCID:0000000264037895), Jones, Andrew C. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA] (ORCID:0009000261992245), Quiter, Brian J. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA] (ORCID:0000000170017455), Nachtsheim, Abigael C. [Los Alamos National Laboratory, Computer, Computational, and Statistical Sciences Division, Los Alamos, NM, USA]. 2026-05-01. RADAI: A Large-Scale Realistic Dataset for Radiation Detection Algorithm Development. https://doi.org/10.1109/tns.2026.3682654
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