Characterizing Plasma Sheet Flows With Machine Learning Identification

B. N. Powers; S. N. F. Chepuri; A. N. Jaynes; A. Lindquist; I. J. Cohen; C. Gabrielse; D. L. Turner

doi:10.1029/2026JA035457

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Characterizing Plasma Sheet Flows With Machine Learning Identification

Journal article

Open access

Peer reviewed

Characterizing Plasma Sheet Flows With Machine Learning Identification

B. N. Powers, S. N. F. Chepuri, A. N. Jaynes, A. Lindquist, I. J. Cohen, C. Gabrielse and D. L. Turner

Journal of geophysical research : Space physics (2013 - Present), Vol.131(7), e2026JA035457

07/20/2026

DOI: 10.1029/2026JA035457

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Abstract

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.

substorms

BBFs

injections

plasma sheet

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Details

Title: Subtitle: Characterizing Plasma Sheet Flows With Machine Learning Identification
Creators: B. N. Powers - University of Iowa
S. N. F. Chepuri - Rice University
A. N. Jaynes - University of Iowa
A. Lindquist - University of Iowa
I. J. Cohen - Johns Hopkins University Applied Physics Laboratory
C. Gabrielse - The Aerospace Corporation
D. L. Turner - Johns Hopkins University Applied Physics Laboratory
Resource Type: Journal article
Publication Details: Journal of geophysical research : Space physics (2013 - Present), Vol.131(7), e2026JA035457
DOI: 10.1029/2026JA035457
ISSN: 2169-9380
eISSN: 2169-9402
Publisher: Wiley
Grant note: Goddard Space Flight Center: NNG04EB99C
This work was supported by funding from the MMS mission, under NASA contract NNG04EB99C. Authors would also like to acknowledge the use of the Space Physics Data Repository at The University of Iowa supported by the Roy J. Carver Charitable Trust. Also, thank you to S. Hill for extensive conversations, and meritorious debate.
Language: English
Date published: 07/20/2026
Academic Unit: Physics and Astronomy; University College Courses
Record Identifier: 9985213549202771

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