Source shape estimation for neutron imaging systems using convolutional neural networks
- Los Alamos National Laboratory 1 , Los Alamos, New Mexico 87544, USA
- Lawrence Livermore National Laboratory 2 , Livermore, California 94550, USA
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:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- 89233218CNA000001
- OSTI ID:
- 2511297
- Report Number(s):
- LA-UR-24-23524
- Journal Information:
- Review of Scientific Instruments, Journal Name: Review of Scientific Instruments Journal Issue: 8 Vol. 95; ISSN 0034-6748
- Country of Publication:
- United States
- Language:
- English
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