around 2 keV) and Ca Kα (around 4 keV) (Lindsay et al. 2016, 2022).

These emissions are driven by keV electrons accelerated in the magnetosphere by magnetic reconnection, as shown by Lavorenti et al. (2023) and discussed in Sec. 3.2.

However, XRS observations had a limited energy resolution and the surface coverage was constrained by MESSENGER’s orbit.

The MIXS instrument onboard BepiColombo (Bunce et al. 2020) will extend XRS observations 11 by providing (i)

more coverage in the southern hemisphere of Mercury, (ii) a higher energy resolution, allowing the separation of the Mg, Al, and Si lines, and (iii) a larger energy range enabling the observations of lines from Na, Fe and O. The novel capabilities of MIXS to detect more and lower energy fluorescence lines, coupled with electron observations from the Mio/MEA instrument (Saito et al. 2021), will help to constraint the energy spectrum and source process of precipitating ∼keV electrons at Mercury. The electron precipitation maps in Fig. 5 coupled with surface composition models will help to interpret the future MIXS observations of electron-induced X-ray fluorescence at Mercury. From the maps in Fig. 5, indirect information on the IMF direction can also be derived from the distribution of X-ray fluorescence onto the surface. Surface charging at Mercury remains an open question at present. From the precipitating fluxes of protons and electrons computed in Sec. 3, we note a clear tendency of the surface to be negatively charged (the electrons mean fluence is around a factor 2 higher than that of protons, as shown in Tab. 1), at least on the nightside of the planet or within shadow in the dayside. This is somewhat expected given the higher mobility of electrons as compared to protons. Nonetheless, at this stage, it is hard to make any definitive conclusions about surface charging from our simulations given (i) the lack of sub-Debye-length kinetic physics in our implicit PIC code, and (ii) the lack of photo-electron emission from the surface of Mercury in our model that is essential to constraining surface charging in illuminated regions of the hermean surface. Future works could address this question using explicit PIC codes resolving the sub-Debye-length plasma-surface interactions as well as including photo-electron emission from the illuminated surface. Protons and electrons fluxes from the maps in this work (in Figs. 3-6) could then be used as boundary conditions for such smaller scale simulations.
5. CONCLUSION From numerical simulations of the interaction of Mercury’s magnetic field with solar wind plasma (protons and electrons), we have computed particle precipitation maps at the planetary surface integrated over two full Mercury orbits (176 Earth days). Our results show that:
• Mercury’s 3:2 spin-orbit resonance has a weak effect on the time-integrated plasma precipitation pattern onto the surface. The surface pattern of precipitating plasma more strongly depends on the upstream magnetic field direction.
• Mercury’s weak magnetic field is able to shield on average 90% of impinging solar wind protons and electrons.
• Mercury’s bow shock and magnetosphere tend to broaden the proton energy distribution, from the solar wind onto the surface. A considerable number of protons at the surface have energies below 500 eV (mostly at high latitudes) and above 1.5 keV (mostly at low latitudes).
• Mercury’s magnetosphere tend to extend to high energies the electron energy distribution, with most of the electrons at the surface found in the range 0.1-4 keV.
Our results demonstrate the complexity of Mercury’s geographical plasma precipitation and paves the way for future quantitative studies addressing (i) space weathering of Mercury’s regolith, (ii) plasma-driven source processes for Mercury’s exosphere such as ion sputtering and ESD, and (iii) electron-induced X-ray fluorescent emission from Mercury’s regolith. The data used throughout the paper are publicly available at Lavorenti et al. (2023).
6. ACKNOWLEDGMENTS FL and PH acknowledges TGCC under the allocations AP010412622 and A0100412428. FL acknowledges the CINECA award under the ISCRA initiative, for the availability of high performance computing resources and support. FL and PH acknowledge the support of CNES for the BepiColombo mission. FL acknowledges ESA support of the PhD. This research was supported by the ISSI in Bern (ISSI International Team project #525). JR was supported by NASA Discovery Data Analysis grant 80NSSC20K1148. SA is supported by JSPS KAKENHI number: 22J01606. DD, EAJ, and DWS are supported by NASA Solar System Workings Grant 80NSSC22K0099.
REFERENCES
Bauch, K. E., Hiesinger, H., Greenhagen, B. T., & Helbert, J. 2021, Icarus, 354, 114083, doi: 10.1016/j.icarus.2020.114083