Strengthening Nuclear Security with ML: Full-Spectrum 137Cs Burial Depth Estimation

Authors

DOI:

https://doi.org/10.53477/2284-9378-25-58

Keywords:

Nuclear Security;, CBRN;, Artificial Intelligence;, Gradient Boosted Decision Trees;, Buried Radiation Sources;

Abstract

The non-intrusive characterization of buried radioactive sources is a critical capability for thwarting illicit trafficking, mitigating orphan‑source hazards, and safeguarding civilian populations against radiological threats. Depth estimation, in particular, enables rapid threat assessment and informed countermeasure deployment following incidents such as transnational uranium diversion or the loss of medical and industrial sources. In this feasibility study, we demonstrate a machine learning approach to estimate the burial depth of a 137Cs point source in dry sand over the range of 5–95 cm. Our method employs gradient-boosted decision trees trained on simulated full gamma-ray spectra partitioned into 1024 energy bins, thereby exploiting subtle variations across both the Compton continuum and multiple photopeaks. After hyperparameter tuning, the model achieved an average depth‐estimation standard deviation of 5 cm across the full depth range. By leveraging the entire spectral profile rather than isolated peak ratios, this
algorithm delivers enhanced accuracy and robustness in heterogeneous field conditions. The results validate the potential of full-spectrum, gradient boosted models as field‑deployable tools for rapid subsurface threat localization, reinforcing layers of nuclear security and environmental monitoring efforts worldwide.

Author Biography

Konstantinos KARAFASOULIS, Laboratory Teaching Staff, Hellenic Army Academy, Athens

Dr Konstantinos Karafasoulis is a member of the Laboratory Teaching Staff at the Hellenic Army Academy and an Associated Researcher at the Aristotle University of Thessaloniki. He earned his BSc in Physics from the Aristotle University (1992) of Thessaloniki and a Ph.D. in High Energy Physics (DELPHI/CERN) from the National Technical University of Athens (1999). With over two decades of expertise, Dr. Karafasoulis specializes in radiation detector simulation, data acquisition systems, and advanced data analysis techniques.

He collaborated on the DELPHI and CMS experiments at CERN, working with teams from Demokritos/Greece and INFN/Italy. Furthermore, Dr. Karafasoulis has contributed to a range of European and National R&D projects focused on refining radiation detectors, data systems, and unique data analysis methods, such as NATO SENERA, FP7-COCAE, EPAN, PYTHAGORAS, PENED, and ESA-C14240. He was the coordinator of the NATO funded project “SENERA: A sensor Network for the localization and Identification of Radiation Sources”.

Dr Karafasoulis was involved in the simulation of the MIDAS device, a space radiation dosimeter, using the GEANT4 framework. He also helped develop reconstruction algorithms to identify and measure energy of particles interacting with the device, as part of an (European Space Agency) ESA funded project. He was also responsible for the GEANT4 simulation Work Package of the “Comprehensive radiation monitor package for Lunar mission,” project sponsored by the European Space Agency (ESA).

Currently, he is the secondary Chief Scientific Investigator of the project “Advancing Nuclear Forensics: Machine Learning methods for Nuclear Crime Scene Investgation” funded by the International Atomic Energy Agency (IAEA). In the context of this project he is leading the research for the development of the Machine/Deep Learning algorithms for the detection and identification of radioactive and Special Nuclear Material (SNM).

Dr Karafasoulis is a certified KNIME Data Scientist  and serves as the Academic Supervisor of the course “KNIME: An introduction to data analysis without programming” offered by the Center for Education and LifeLong Learning of the Aristotle University of Thessaloniki, where he enjoys teaching about data analysis.

 

For a complete list of publications, see: http://karafasoulis.eu/

 

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Published

2026-01-19

How to Cite

KARAFASOULIS, K. . (2026). Strengthening Nuclear Security with ML: Full-Spectrum 137Cs Burial Depth Estimation. BULLETIN OF "CAROL I" NATIONAL DEFENCE UNIVERSITY, 14(4), 53–64. https://doi.org/10.53477/2284-9378-25-58

Issue

Vol. 14 No. 4 (2025)

Section

Articles

License

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