An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data Article Swipe
D. C. P. Kuruppuaratchi
,
J. Gruesbeck
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3847/1538-4365/adf2a2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3847/1538-4365/adf2a2
A neural network model known as ParkerNet has been implemented for classifying switchbacks in Parker Solar Probe (PSP) data. ParkerNet is a binary classification neural network model that combines convolutional neural network layers with bidirectional long short-term memory layers. We employ a targeted, human-in-the-loop approach, where a small set of labels is initially provided to the network for training, and select predictions are iteratively corrected and fed back for retraining. The predictions from the network are compared to two switchback catalogs by J. Huang et al. and F. Pecora et al. ParkerNet only needed approximately 12% of data labeled as switchbacks to demonstrate strong performance, showing high agreement with the core/spike region in the J. Huang et al. catalog. The application of ParkerNet to PSP data highlights the potential of a data-forward approach to unify the identification and characterization of switchbacks and provides a framework for future switchback detection.
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- Type
- article
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- Landing Page
- https://doi.org/10.3847/1538-4365/adf2a2
- OA Status
- gold
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- 58
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- 10
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https://openalex.org/W4414027387Canonical identifier for this work in OpenAlex
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https://doi.org/10.3847/1538-4365/adf2a2Digital Object Identifier
- Title
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An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe DataWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-01Full publication date if available
- Authors
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D. C. P. Kuruppuaratchi, J. GruesbeckList of authors in order
- Landing page
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https://doi.org/10.3847/1538-4365/adf2a2Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3847/1538-4365/adf2a2Direct OA link when available
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Computer science, Environmental science, Artificial intelligence, Remote sensing, GeographyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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58Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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