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Characterizing Plasma Sheet Flows With Machine Learning Identification
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DOI：10.1029/2026ja035457.png)
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Abstract
En 中文
Bursty bulk flows, injections, and related flows are a key component of plasma energy and flux transport during substorms. Mixed observational features require researchers to carefully define their quantitative criteria in studies of such events, a process that requires presuming some quantity of the output events. In this work we present a novel supervised random forest classification model for identifying substorm related flows in the plasma sheet. Our model allows a more complete view of event variety, and avoids the presumptive nature of data-derived quantitative classification. We observe no correlation between event velocities and geomagnetic activity, however an uneven formation of large-scale dipolarization in the tail region during strong substorms is statistically present in our data. A bi-modality also appears in the event occurrence location during strong substorms. Some model limitations and future work is discussed.
Keywords:
substorms
BBFs
injections
plasma sheet
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