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dc.contributor.authorWoods, B. J. Q.-
dc.contributor.authorDuarte, V. N.-
dc.contributor.authorFredrickson, E. D.-
dc.contributor.authorGorelenkov, N. N.-
dc.contributor.authorPodestà, M.-
dc.contributor.authorVann, R. G. L.-
dc.description.abstractAbrupt large events in the Alfvenic and sub-Alfvenic 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 behavior of magnetic perturbations from corresponding frequency spectrograms that are typically identified by humans. The analysis allows for comparison between different mode character (such as quiescent, fixed frequency, and chirping, avalanching) and plasma parameters obtained from the TRANSP code, such as the ratio of the neutral beam injection (NBI) velocity and the Alfven velocity (v_inj./v_A), the q-profile, and the ratio of the neutral beam beta and the total plasma beta (beta_beam,i / beta). In agreement with the previous work by Fredrickson et al., we find a correlation between beta_beam,i and mode character. In addition, previously unknown correlations are found between moments of the spectrograms and mode character. Character transition from quiescent to nonquiescent behavior for magnetic fluctuations in the 50200-kHz frequency band is observed along the boundary v_phi ~ (1/4)(v_inj. - 3v_A), where v_phi is the rotation velocity.en_US
dc.description.tableofcontentsreadme and digital data filesen_US
dc.publisherPrinceton Plasma Physics Laboratory, Princeton Universityen_US
dc.relationIEEE Transactions on Plasma Scienceen_US
dc.subjectmachine learningen_US
dc.subjecttokamak physicsen_US
dc.subjectplasma physicsen_US
dc.titleMachine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTXen_US
dc.contributor.funderU. S. Department of Energyen_US
Appears in Collections:NSTX-U

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