Automated isotope identification algorithm using artificial neural networks

Kamuda, Mark M.

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https://hdl.handle.net/2142/97440 Copy

Description

Title

Automated isotope identification algorithm using artificial neural networks

Author(s)

Kamuda, Mark M.

Issue Date

2017-04-25

Director of Research (if dissertation) or Advisor (if thesis)

Sullivan, Clair J.

Committee Member(s)

Huff, Kathryn

Department of Study

Nuclear, Plasma, & Rad Engr

Discipline

Nuclear, Plasma, Radiolgc Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Date of Ingest

2017-08-10T19:15:56Z

Keyword(s)

• Automated isotope identification
• Artificial neural networks

Abstract

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.

Graduation Semester

2017-05

Type of Resource

text

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

Copyright and License Information

Copyright 2017 Mark Kamuda

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