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Source shape estimation for neutron imaging systems using convolutional neural networks

Journal Article · · Review of Scientific Instruments
DOI:https://doi.org/10.1063/5.0214449· OSTI ID:2453958
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  1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
  2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
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Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
2453958
Alternate ID(s):
OSTI ID: 2511297
Report Number(s):
LLNL--JRNL-869909; 1106554
Journal Information:
Review of Scientific Instruments, Journal Name: Review of Scientific Instruments Journal Issue: 8 Vol. 95; ISSN 0034-6748
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

References (10)

Handcrafted vs. non-handcrafted features for computer vision classification journal November 2017
Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment journal November 2020
Modeling the National Ignition Facility neutron imaging system journal October 2010
The neutron imaging diagnostic at NIF (invited) journal October 2012
Neutron source reconstruction from pinhole imaging at National Ignition Facility journal February 2014
Self characterization of a coded aperture array for neutron source imaging journal December 2014
A liquid VI scintillator cell for fast-gated neutron imaging journal October 2018
Source localization for neutron imaging systems using convolutional neural networks journal June 2024
Lawson Criterion for Ignition Exceeded in an Inertial Fusion Experiment journal August 2022
Developments in Image Processing Using Deep Learning and Reinforcement Learning journal September 2023

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Related Subjects

42 ENGINEERING
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
70 PLASMA PHYSICS AND FUSION TECHNOLOGY
73 NUCLEAR PHYSICS AND RADIATION PHYSICS
artificial neural networks
convolutional neural network
estimation theory
image processing
neutron flux
neutron imaging
signal processing