Automated Data Processing of Neutron Depth Profiling Spectra using an Artificial Neural Network

Author

Mubarak Albarqi
Raed Alsulami
Joseph T. Graham, Missouri University of Science and TechnologyFollow

Abstract

This work examines the possibility of using an Artificial Neural Network (ANN) to interpret Neutron Depth Profiling (NDP) spectra. AMonte Carlo of N-Particle (MCNP6) radiation transport model was used to simulate the alpha energy spectrum of NIST standard SRM 2137 during a ¹⁰B(n, α)⁷ Li NDP measurement. Simulations of 300 randomly generated specimens were also performed and used to train an Artificial Neural Network (ANN). The depth profile of boron in the SRM2137 NIST standard was obtained by processing the MCNP pulse height light tally with the trained ANN. This was compared to the results of traditional analysis using stopping tables. The traditional analysis and ANN results both agree well with the reference SRM 2137 boron profile.

Recommended Citation

M. Albarqi et al., "Automated Data Processing of Neutron Depth Profiling Spectra using an Artificial Neural Network," Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 953, Elsevier B.V., Feb 2020.

The definitive version is available at https://doi.org/10.1016/j.nima.2019.163217

Department(s)

Nuclear Engineering and Radiation Science

Keywords and Phrases

ANN; Artificial Neural Network; MCNP; Monte Carlo; Neutron Depth Profiling

International Standard Serial Number (ISSN)

0168-9002

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Elsevier B.V., All rights reserved.

Publication Date

01 Feb 2020