Machine learning characterisation of Alfv\'{e}nic and sub-Alfv\'{e}nic chirping and correlation with...
Machine learning characterisation of Alfv\'{e}nic and sub-Alfv\'{e}nic chirping and correlation with fast ion loss at NSTX
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Abrupt large events in the Alfv\'{e}nic and sub-Alfv\'{e}nic frequency bands in tokamaks are typically correlated with increased fast ion loss. Here, machine learning is used to speed up the laborious process of characterizing the behaviour of magnetic perturbations from corresponding frequency spectrograms that are typically identified by humans....
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Machine learning characterisation of Alfv\'{e}nic and sub-Alfv\'{e}nic chirping and correlation with fast ion loss at NSTX
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TN_cdi_proquest_journals_2191252948
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2191252948
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2331-8422
