Work

A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification

Year: 2025

Type: article

Abstract: Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

Source: Electronics

DOI: 10.3390/electronics14081688

Authors: Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Institutions: Applied Research (United States), Thomas Jefferson National Accelerator Facility, Kennesaw State University, Old Dominion University

Language: English

Topic: Radiation Detection and Scintillator Technologies

Subfield: Radiation

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Johns Hopkins University