Estimating Interplanetary Magnetic Field Conditions at Mercury's Orbit From MESSENGER Magnetosheath Observations Using a Feedforward Neural Network

Bowers, Charles F.; Jackman, Caitríona M.; Azari, Abigail R.; Smith, Andy W.; Wright, Paul J.; Rutala, Matthew J.; Sun, Weijie; Healy, Adam

doi:10.1029/2024JH000239

Title: Estimating Interplanetary Magnetic Field Conditions at Mercury's Orbit From MESSENGER Magnetosheath Observations Using a Feedforward Neural Network

Journal Article · 21 October 2024 · Journal of Geophysical Research. Machine Learning and Computation (Online)

DOI: https://doi.org/10.1029/2024JH000239 · OSTI ID:2483781

^[1];

^[2];

^[3]; Wright, Paul J. ^[1];

^[1];

^[4]; Healy, Adam ^[1]

School of Cosmic Physics DIAS Dunsink Observatory Dublin Institute for Advanced Studies Dublin Ireland
Department of Earth, Ocean and Atmospheric Sciences University of British Columbia Vancouver BC Canada
Department of Mathematics, Physics and Electrical Engineering Northumbria University Newcastle UK
Space Sciences Laboratory University of California Berkeley CA USA

Abstract Mercury's small magnetosphere is embedded in the dynamic and intense solar wind environment characteristic of the inner heliosphere. Both the magnitude and orientation of the interplanetary magnetic field (IMF) significantly influence the solar wind‐magnetospheric interaction at Mercury, driving phenomena such as magnetic reconnection. The MErcury Surface, Space Environment, Geochemistry and Ranging (MESSENGER) spacecraft provided in‐situ magnetic field measurements of the solar wind, the magnetosheath, and the magnetosphere along each orbit. However, it is a challenge to directly assess the IMF's impact on Mercury's plasma environment due to the temporal separation between observations within the solar wind and the magnetosphere, especially in the absence of an upstream monitor. Here, we present a feedforward neural network (FNN) trained on a subset of magnetosheath observations to estimate the strength and orientation of the IMF upstream of the bow shock. Utilizing magnetosheath magnetic field, cylindrical spatial coordinates, and heliocentric distance measurements, the FNN predicts upstream IMF conditions with an score of 0.70 and mean averaged error of 5.3 nT, thereby greatly decreasing the temporal separation between IMF estimates and magnetospheric measurements throughout the MESSENGER mission. This approach yields IMF estimates for all magnetosheath data measured by MESSENGER, providing a useful tool for future investigations of the IMF impact on Mercury's magnetosphere. This method will be integrable with the dual‐spacecraft BepiColombo magnetosheath measurements, providing useful estimates of upstream IMF conditions particularly during the extended periods in which neither spacecraft sample the solar wind. Our results demonstrate the utility of machine learning techniques on advancing space science research.

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Sponsoring Organization:: USDOE

OSTI ID:: 2483781

Journal Information:: Journal of Geophysical Research. Machine Learning and Computation (Online), Journal Name: Journal of Geophysical Research. Machine Learning and Computation (Online) Journal Issue: 4 Vol. 1; ISSN 2993-5210

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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