Machine Learning Approach for Gamma-ray Spectra Identification for Radioactivity Analysis

T. Kin, J. Goto, M. Oshima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We have proposed a machine learning model for efficient gamma-ray spectrometry for environmental recovery from the Fukushima Daiichi Power Plant Accident. In the present study, we focus on a radioactive nuclide identification by the machine learning in a screening measurement. A simple deep neural network having two hidden layer is proposed, and the identification accuracy is achieved more than 95% for single gamma-ray spectra.

Original languageEnglish
Title of host publication2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141640
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019 - Manchester, United Kingdom
Duration: Oct 26 2019Nov 2 2019

Publication series

Name2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019

Conference

Conference2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
CountryUnited Kingdom
CityManchester
Period10/26/1911/2/19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Radiology Nuclear Medicine and imaging
  • Nuclear and High Energy Physics

Fingerprint Dive into the research topics of 'Machine Learning Approach for Gamma-ray Spectra Identification for Radioactivity Analysis'. Together they form a unique fingerprint.

  • Cite this

    Kin, T., Goto, J., & Oshima, M. (2019). Machine Learning Approach for Gamma-ray Spectra Identification for Radioactivity Analysis. In 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019 [9059618] (2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSS/MIC42101.2019.9059618