A Hybrid Deep Learning based Algorithm for Gamma Spectroscopy Analysis
Date
2025-03
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : Stellenbosch University
Abstract
There exists a select group of unstable nuclei which undergo radioactive decay
by emission of highly-energetic photons or γ-rays. An effective tool for
analysing γ-emitting radioactive sources, γ spectroscopy is a common experimental
technique used in high-energy physics and environmental monitoring
of radiation. Analysis of various naturally-occurring radionuclides and their
decay products through γ spectroscopy has provided a practice for health and
safety regulation. It is a process which has seen a large amount of improvement
in efficiency and optimization over the past decade. This has often
been partly due to developments in detection and analysis which build upon
existing experimental methods. Recently, there have been developments in
machine learning based approaches, for automated and efficient detection and
radioisotope "fingerprinting". That is, these methods make use of the characteristic
spectra measured from radioactive isotopes to identify them. These
methods have commonly consisted mostly of a computational component, a
deep-learning algorithm known generally as a deep neural network (DNN),
which is trained on a representative dataset of energy spectra from different
isotopes, often simulation-based. Becoming increasingly more efficient, a type
of network called convolutional neural networks (CNNs) are most prominent in
this application, due to their ability to effectively learn useful features from
high-quality data and being able to classify or recognise radioactive species
with very good accuracy. However, a challenge which can occur is when there
are multiple sources present in a sample of data, as opposed to just a single
source, where the accuracy of the network will often decrease as the number of
sources increases. Additionally, developing a CNN model which is computationally
efficient, but also accurate and robust to noise and other environmental
influences can be difficult.
Description
Thesis (PhD)--Stellenbosch University, 2025.
Buckton, C. 2025. A Hybrid Deep Learning based Algorithm for Gamma Spectroscopy Analysis. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/8db1d4f0-1872-4d33-87fd-8b3527a44704
Buckton, C. 2025. A Hybrid Deep Learning based Algorithm for Gamma Spectroscopy Analysis. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/8db1d4f0-1872-4d33-87fd-8b3527a44704