Neural networks for reconstruction and uncertainty quantification of fast-ion phase-space distributions using FILD and INPA measurements

Neural networks for reconstruction and uncertainty quantification of fast-ion phase-space distributions using FILD and INPA measurements

This study introduces the use of a deep convolutional neural network for reconstructing fast-ion velocity distributions from fast-ion loss detectors and imaging neutral particle analyzers (INPAs), automatically integrating uncertainty quantification through Monte Carlo dropout. The network-based rec...

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Bibliographic Details
Main Authors: B. S. Schmidt, J. Rueda-Rueda, J. Galdon-Quíroga, M. García-Muñoz, P. A. Schneider, M. Salewski, the ASDEX Upgrade Team
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Nuclear Fusion
Subjects:
neural networks
fast-ion diagnostics
uncertainty quantification
tomographic reconstruction
high-energy feature
pitch-angle splitting
Online Access:https://doi.org/10.1088/1741-4326/ad93e6
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https://doi.org/10.1088/1741-4326/ad93e6

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