Source localization for neutron imaging systems using convolutional neural networks
The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference...
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Published in | Review of scientific instruments Vol. 95; no. 6 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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United States
01.06.2024
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Abstract | The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium–tritium shots. |
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AbstractList | The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium-tritium shots. |
Author | Fittinghoff, David Fatherley, Valerie Danly, Chris Geppert-Kleinrath, Verena Mendoza, Emily Freeman, Matthew S. Durocher, Mora Tafoya, Landon Volegov, Petr Rubery, Michael Wilde, Carl Saavedra, Gary |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38888398$$D View this record in MEDLINE/PubMed |
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