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Source localization for neutron imaging systems using convolutional neural networks.
- Source :
-
Review of Scientific Instruments . Jun2024, Vol. 95 Issue 6, p1-10. 10p. - Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00346748
- Volume :
- 95
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Review of Scientific Instruments
- Publication Type :
- Academic Journal
- Accession number :
- 178147133
- Full Text :
- https://doi.org/10.1063/5.0205472