Deep learning on simulated gamma spectra for explosives detection using a NaI detector

Authors

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

DOI:

https://doi.org/10.53477/2284-9378-25-01

Keywords:

Explosives Detection, Artificial Intelligence, Neutron Activation, Gamma Radiation.

Abstract

The detection of explosives and contraband materials using neutron activation analysis (NAA) is a critical component of modern security systems. This study investigates the feasibility of identifying explosive materials using a simple sodium iodide (NaI) scintillation detector limited to a 3 MeV gamma energy range. The detector’s limitations pose a significant challenge as characteristic gamma photopeaks above this range, such as those near 10 MeV, are excluded. Utilising a 14 MeV neutron source, gamma spectra from simulated neutron interactions with explosive materials were analysed using Geant4. This work demonstrates that with advanced machine learning models, such as convolutional neural networks (CNNs) and tailored data preprocessing methods, effective discrimination between explosives and non-explosives is achievable despite these constraints.

Author Biography

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

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/

 

 

References

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Nunes, W.V., A.X. da Silva, V.R. Crispim, and R. Schirru. 2002. “Explosives detection using prompt-gamma neutron activation and neural networks.” Applied Radiation and Isotopes 56: 937-943.

S. Agostinelli, S, J Allison, K Amako, J Apostolakis, H Araujo, P Arce, M Asai, et al. 2003. “Geant4—a simulation toolkit.” Nuclear Instruments and Methods A 506: 250-303. doi:10.1016/S0168-9002(03)01368-8.

Whetstone, Z.D., and K.J. Kearfott. 2014. “A review of conventional explosives detection using active neutron interrogation.” Radioanalytical and Nuclear Chemistry 301: 629-639. doi:10.1007/s10967-014-3260-5.

Zehtabvar, Mehrnaz, Kazem Taghandiki, Nahid Madani, Dariush Sardari, and Bashir Bashiri. 2024. “A Review on the Application of Machine Learning in Gamma Spectroscopy: Challenges and Opportunities.” Spectroscopy Journal 2: 123-144. doi:10.3390/spectroscj2030008.

Funding Information

The author declares that no funding or financial support was received from any organization, institution, or individual for the research, design, execution, or writing of this work.

Conflict of Interest

The author declares no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The data that support the findings of this study are openly available in the Open Science Framework at https://osf.io/wh8n4.

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Published

2025-04-03

How to Cite

KARAFASOULIS, K. . (2025). Deep learning on simulated gamma spectra for explosives detection using a NaI detector. BULLETIN OF "CAROL I" NATIONAL DEFENCE UNIVERSITY, 14(1), 7–18. https://doi.org/10.53477/2284-9378-25-01

Issue

Vol. 14 No. 1 (2025)

Section

Articles

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