Identifying MESSENGER Magnetospheric Boundary Crossings Using a Random Forest Region Classifier

Abstract We present a new list of bow shock and magnetopause crossings based on automated region classification for the MESSENGER (Mercury Surface, Space Environment, Geochemistry and Ranging) mission. We fit a random forest model to magnetometer and ephemeris data to classify the solar wind, magnetosheath, and magnetosphere regions surrounding Mercury. The random forest was highly accurate when predicting the testing data set, with an accuracy of . We apply this model to the orbital phase (March 2011 to April 2015) of the MESSENGER mission, and determine crossings automatically where changes in region classification occur. We apply several post‐processing steps to this list of crossings and determine 19,989 bow shock crossings and 11,414 magnetopause crossings for the MESSENGER mission. We find good visual comparison with the spatial distribution of these new crossings and existing data sets of boundary crossing intervals.

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Journal
Journal of Geophysical Research Machine Learning and Computation
Published
2026-02-01
DOI
https://doi.org/10.1029/2025jh000921
Primary Topic
Ionosphere and magnetosphere dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

Identifying MESSENGER Magnetospheric Boundary Crossings Using a Random Forest Region Classifier

A. R. Fogg, Daragh M. Hollman, C. F. Bowers, Simon James Walker et al.
Journal of Geophysical Research Machine Learning and Computation
Ionosphere and magnetosphere dynamics
article

Identifying MESSENGER Magnetospheric Boundary Crossings Using a Random Forest Region Classifier

A. R. Fogg, Daragh M. Hollman, C. F. Bowers, Simon James Walker, Matthew Rutala, Katarina Domijan, Caitríona M. Jackman
article en

Abstract

Abstract We present a new list of bow shock and magnetopause crossings based on automated region classification for the MESSENGER (Mercury Surface, Space Environment, Geochemistry and Ranging) mission. We fit a random forest model to magnetometer and ephemeris data to classify the solar wind, magnetosheath, and magnetosphere regions surrounding Mercury. The random forest was highly accurate when predicting the testing data set, with an accuracy of . We apply this model to the orbital phase (March 2011 to April 2015) of the MESSENGER mission, and determine crossings automatically where changes in region classification occur. We apply several post‐processing steps to this list of crossings and determine 19,989 bow shock crossings and 11,414 magnetopause crossings for the MESSENGER mission. We find good visual comparison with the spatial distribution of these new crossings and existing data sets of boundary crossing intervals.

Journal of Geophysical Research Machine Learning and ComputationVol. 3(1)
Dublin Institute For Advanced Studies (IE), National University of Ireland, Maynooth (IE), University of Michigan (US)
Science Foundation Ireland
Life in Land
Openalex Percentile: Top 56%
Ionosphere and magnetosphere dynamics
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Identifying MESSENGER Magnetospheric Boundary Crossings Using a Random Forest Region Classifier — A. R. Fogg, Daragh M. Hollman, et al. · Journal of Geophysical Research Machine Learning and Computation (2026) | TGRS Research Map | TGRS