Automated isotope identification algorithm using artificial neural networks

There is a need to develop an algorithm that can determine the relative activities of a mixture of many isotopes in a low-resolution gamma-ray spectrum. While techniques for this task exist, they require a human operator and are too slow to use on very large datasets of spectra. Pattern recognition algorithms such as neural networks are prime candidates for automated isotope identification using low-resolution gamma-ray spectra. While algorithms based on feature extraction such as peak finding or ROI algorithms work well for well calibrated high resolution detectors, for low-resolution detectors it may be more beneficial to use algorithms that incorporate more abstract features of the spectrum. This is especially true when analyzing a mixture of isotopes where peak overlap and Compton continuum effects occlude features of interest. To solve this, an artificial neural network (ANN) was trained to predict the presence and relative activities of isotopes from a mixture of many isotopes. The ANN is trained with simulated gamma-ray spectra, allowing easy expansion of the library of target isotopes. In this thesis, an algorithm based on an ANN is presented and evaluated against a series of measured spectra.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Nuclear, Plasma, Radiolgc Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Kamuda, Mark M.

Contributors dc:contributor

• Sullivan, Clair J.
• Huff, Kathryn

• Automated isotope identification
• Artificial neural networks

Statement dc:rights

• Copyright 2017 Mark Kamuda

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/97440

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/97440