A Virtual Solar Wind Monitor at Mars With Uncertainty Quantification Using Gaussian Processes

• Published in: Journal of geophysical research. Machine learning and computation Vol. 1; no. 3
• Main Authors: Azari, A. R., Abrahams, E., Sapienza, F., Halekas, J., Biersteker, J., Mitchell, D. L., Pérez, F., Marquette, M., Rutala, M. J., Bowers, C. F., Jackman, C. M., Curry, S. M.
• Format: Journal Article
• Language: English
• Published: 01.09.2024
• Subjects: Gaussian processes; heliopshere; Mars; planetary; solar wind; uncertainty quantification
• ISSN: 2993-5210; 2993-5210
• DOI: 10.1029/2024JH000155

Single spacecraft missions do not measure the pristine solar wind continuously because of the spacecrafts' orbital trajectory. The infrequent spatiotemporal cadence of measurement fundamentally limits conclusions about solar wind‐magnetosphere coupling throughout the solar system. At Mars, such single spacecraft missions result in limitations for assessing the solar wind's role in causing lower altitude observations, such as auroral dynamics or atmospheric loss. In this work, we detail the development of a virtual solar wind monitor from the Mars Atmosphere and Volatile Evolution (MAVEN) mission; a single spacecraft. This virtual solar wind monitor provides a continuous estimate of the solar wind upstream from Mars with uncertainties. We specifically employ Gaussian process regression to estimate the upstream solar wind and uncertainty estimations that scale with the data sparsity of our real observations. This proxy enables continuous solar wind estimation at Mars with representative uncertainties for the majority of the time since late 2014. We conclude by discussing suggested uses of this virtual solar wind monitor for statistical studies of the Mars space environment and heliosphere. Plain Language Summary When a spacecraft orbits a planet, it travels through multiple spatial regions and it can be a long time between subsequent measurements of a region. This makes it difficult to understand how one region affects another as two regions are never measured at the same time. This is the scenario that the orbiting MAVEN spacecraft is in at Mars when measuring the solar wind. It is commonly accepted that the solar wind conditions, including magnetic field, velocity, and density, affects a planet's space environment. However, because of MAVEN's orbit, there is a large amount of uncertainty when estimating how the solar wind affects physical processes like atmospheric loss and auroral formation. In this work, we create a continuous estimation, or virtual monitor, of the solar wind from MAVEN measurements. We do this by applying a machine learning method to estimate solar wind parameters and a predicted confidence, or uncertainty, in these estimates. These uncertainties increase as MAVEN obtains less sampling and our confidence in the solar wind prediction decreases. We conclude this work by sharing the suggested usage of this method in future studies of Mars and the heliosphere. Key Points We develop a continuous estimate of the solar wind upstream of Mars using Gaussian process regression on Mars Atmosphere and Volatile Evolution data This model enables solar wind estimation at Mars with an R2 ≥ of 0.95 for 66% of the time since late 2014 This solar wind proxy is best used for the robust statistical studies of Mars' space environment and the heliosphere