Source shape estimation for neutron imaging systems using convolutional neural networks.
| Title: | Source shape estimation for neutron imaging systems using convolutional neural networks. |
|---|---|
| Authors: | Saavedra G; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.; Geppert-Kleinrath V; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.; Danly C; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.; Durocher M; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.; Wilde C; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.; Fatherley V; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.; Mendoza E; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.; Tafoya L; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.; Volegov P; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.; Fittinghoff D; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.; Rubery M; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.; Freeman MS; Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA. |
| Source: | The Review of scientific instruments [Rev Sci Instrum] 2024 Aug 01; Vol. 95 (8). |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: American Institute Of Physics Country of Publication: United States NLM ID: 0405571 Publication Model: Print Cited Medium: Internet ISSN: 1089-7623 (Electronic) Linking ISSN: 00346748 NLM ISO Abbreviation: Rev Sci Instrum Subsets: MEDLINE; PubMed not MEDLINE |
| Imprint Name(s): | Publication: 1933- : Woodbury, N.Y. : American Institute Of Physics; Original Publication: 1930-1932 : Menasha, WI : Optical Society of America |
| Abstract: | 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.; (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).) |
| Entry Date(s): | Date Created: 20240829 Latest Revision: 20240829 |
| Update Code: | 20260130 |
| DOI: | 10.1063/5.0214449 |
| PMID: | 39207189 |
| Database: | MEDLINE |
Journal Article