Deep Neural Networks for Surface Composition Reconstruction from In Situ Exospheric Measurements at Mercury

Abstract:

The exosphere of Mercury is the result of various processes and interactions of the surface with the planetary environment. The external factors acting on the planet, such as dust particles, solar wind protons and heavy ions, solar radiation and intense heat affect the composition and dynamics of the exosphere [1]. In turn, in situ exospheric measurements could allow us to derive surface information and complement surface mapping provided by devoted imagers, thus giving us additional information on the surface release processes, the dynamical interactions with the planetary environment, the erosion, space weathering and, eventually, about the evolution of the planet. At least two of the surface release processes - the micrometeorite vaporization and the ion sputtering - could serve as valid indicators of the regolith composition below, as they are stochiometric energetic surface release processes [2].

We hereby examine a tentative proxy method to derive the elemental and mineralogical composition of the regolith of Mercury from in situ measurements of its neutral exosphere through the use of deep neural networks (DNNs) [3]. We present a multivariate regression (MVR) supervised feed-forward DNN architecture whose inputs are the exospheric densities and proton precipitation fluxes measured in mock-up orbital runs through simulated Hermean exospheres [4] in view of the analysis of data from the SERENA (Search for Exospheric Refilling and Emitted Natural Abundances) instrument package on-board the Mercury Planetary Orbiter - part of the BepiColombo space mission to Mercury, nominal phase starting in 2026 [5]. The primary analysis task of the supervised learning algorithm is to predict from those exospheric measurements the constitution of the surface regolith below in terms of chemical elements and mineralogy. We show that, by learning from example, the DNNs can estimate the data generation mechanisms and allow us to omit the detailed analytical description of all the processes at the surface and in the exosphere, while at the same time give us a good approximation of the highly non-linear relationships between variables characterizing the release processes.

We further explore how the development of this method into nested DNNs could aid the constraining of the exopheric generation models and could give us even more insight into the interaction between the environment, the planetary surface and the exosphere. This way, we aim to add an artificial intelligence tool to our toolbox in the analysis of planetary data, which could be give us a new point of view on the exospheric measurements, thus breaking new ground for interpretations. [1] Milillo et al. 2005, SSR, 117, 397-443. [2] Killen et al. 2007, SSR, 132, (2–4): 433–509. [3] Kazakov et al. 2020, J. Phys.: Conf. Ser., 154, 12-14. [4] Mura 2005, PSS, 55, 1569–1583. [5] Orsini et al. 2020, SSR.