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

• Published in: Journal of geophysical research. Machine learning and computation Vol. 1; no. 4
• Main Authors: Bowers, Charles F., Jackman, Caitríona M., Azari, Abigail R., Smith, Andy W., Wright, Paul J., Rutala, Matthew J., Sun, Weijie, Healy, Adam
• Format: Journal Article
• Language: English
• Published: United States American Geophysical Union (AGU) 01.12.2024; Wiley
• Subjects: IMF; magnetosphere; neural network
• ISSN: 2993-5210; 2993-5210
• DOI: 10.1029/2024JH000239

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 r2 ${\mathit{r}}^{2}$ 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. Plain Language Summary The stream of electrically charged gas emitted from the Sun, known as the solar wind, encounters planetary objects within its flow. The solar wind carries the interplanetary magnetic field (IMF), which has a great influence on the dynamics that govern the interaction between the solar wind and a planetary environment. At Mercury, the solar wind is particularly strong, and the properties of the IMF drive a variety of important physical processes at the planet. The MESSENGER spacecraft orbited Mercury from 2011 to 2015 and sampled both the solar wind and the near‐Mercury environment along each orbit. However, it is difficult to directly measure the IMF's effect on Mercury's environment because there is a gap in time between observations in these different regions. In this study, we use a machine‐learning model to estimate the IMF's strength and direction just before it reaches Mercury, based on data from the region between Mercury's magnetic field and the solar wind. This allows us to fill in the gaps in between solar wind measurements and better understand how the IMF affects Mercury's environment. The model is accurate in predicting the IMF conditions, which provides valuable information for studying Mercury's magnetosphere with both past and future spacecraft missions. Key Points A feedforward neural network (FNN) is trained to predict interplanetary magnetic field (IMF) properties from Mercury magnetosheath observations made by MESSENGER The model achieves high accuracy (r2 = 0.70) predictions for the IMF from magnetosheath measurements The FNN could be integrated with BepiColombo measurements, yielding IMF estimates for the extended periods without solar wind observations