Effects of and pickup ions on the lunar-like plasma environment: 3D hybrid modeling
Introduction
The hybrid kinetic model used here supports comprehensive modeling of the interaction between different spatial and energetic elements of the Moon-solar wind-magnetosphere of the Earth system. This involves variable upstream magnetic field and solar wind plasma, including energetic ions, electrons, and neutral atoms. This capability is critical to improved interpretation of existing measurements for surface and exospheric composition from previous missions and planning future missions.
Lunar observations show the existence of several species of the neutrals and pickup ions like etc., (see e.g. Tyler et al., 1988, Potter and Morgan, 1988, Tanaka et al., 2009, Hartle and Killen, 2006). Hartle and Killen (2006) have provided the measurable lower limits of exosphere densities with currently known upper limits inside exosphere: , and . Recently, MAP-PAGE-IMA (Plasma energy Angle and Composition Experiment, and Ion Mass Analyzer) onboard Japanese lunar orbiter SELENE (KAGUYA) detected Moon originating ions at 100 km altitude. Ion species of , , and were definitively identified. The Solar Wind Ion Detectors (SWIDs) on the Chang’E-1 spacecraft, while orbiting the Moon, occasionally observed two continuous flux peaks with energies not exceeding 8 and 4 times that of the prevailing solar wind energy (Wang et al., 2011).
There is a set of MHD (Wolf, 1968, Spreiter et al., 1970), kinetic (Birch and Chapman, 2001), hybrid (Kallio, 2005, Travnichek et al., 2005, Lipatov and Cooper, 2010, Wiehle et al.,, 2011, Holmström et al., 2012, Wang et al., 2011), drift kinetic (Whang, 1969, Whang and Ness, 1970, Catto, 1974, Lipatov, 1976, Lipatov, 2002, Lipatov et al., 2005), and electrostatic (Farrell et al., 1998, Tao et al., 2012) modeling of the lunar plasma environment.
Wave-particle interactions play a very important role in plasma dynamics near the Moon: mass loading, excitation of low-frequency waves and the formation of the non-Maxwellian particle velocity distribution function. Particle-wave interactions play a very important role in the possible formation of an oblique shock wave system inside the lunar plasma wake, and in coupling of pickup ions and the upstream ions via excitation of low-frequency waves. These kinetic processes become important in the formation of an obstacle for the upstream flow.
Magnetohydrodynamic (MHD) models have been useful for the study of the interaction between plasma flow and the Moon (Wolf, 1968, Spreiter et al., 1970). MHD modeling demonstrated a global picture of magnetospheric interaction with the Moon, formation of the plasma wake with external rarefaction waves and oblique shock structures. However, several kinetic effects are not included in the MHD formalism, namely: anisotropy of the ion velocity distribution which results to excitation of the low-frequency electromagnetic waves, formation of the electron and ion beams and excitation of the high-frequency waves, etc. Many of these effects may be recovered by using hybrid or full kinetic modeling.
In papers by Whang, 1969, Whang and Ness, 1970, Lipatov, 1974, Lipatov, 1975, Lipatov, 1976, they had studied the structure of the lunar plasma wake in the “guiding center” approximation. These models produced the magnetic field perturbations which are in a good agreement with onboard observations of the lunar wake by the Explorer 35 spacecraft. The “guiding center-in-cell” numerical modeling (Lipatov, 1976) also produced the magnetic filed perturbation in the case of nonstationary solar wind and the conducting lunar core. A quasi-MHD (Chew–Goldberger–Low) approach (Chew et al., 1956)) with anisotropic pressure has also described well the electromagnetic perturbations in the lunar wake (Catto, 1974). Several 3D hybrid modeling of the Moon plasma interactions were performed during the last decade as described in papers by Kallio, 2005, Travnichek et al., 2005, Lipatov et al., 2005, Lipatov and Cooper, 2010, Wiehle et al.,, 2011, Holmström et al., 2012. These models describe well wave-particle interactions, in particular, the anisotropy of the ion velocity distributions. The hybrid models demonstrate the formation of the oblique shock-like structure in the middle of the lunar wake. The hybrid modeling by Wiehle et al., (2011) has been devoted to an interpretation of the ARTEMIS data and good agreement was produced.
The hybrid kinetic model allows us to take into account the finite gyroradius effects of pickup ions and to correctly estimate the ion velocity distribution and the fluxes along the magnetic field, and on the lunar surface. Modeling shows the formation of the asymmetric Mach cone, the structuring of the pickup ion tails, and presents another type of lunar-solar wind interaction. We will compare the results of our modeling with observed distributions.
In our study the model of the neutral exosphere are chosen from Hartle and Killen (2006). Note, that we already performed the modeling the dynamics of pickup ions near the Moon (Lipatov et al., 2011). The solar wind parameters are chosen from the ARTEMIS observations (Wiehle et al.,, 2011). We apply a time-dependent Boltzmann’s “particle-in-cell” approach (Lipatov et al., 1998), together with a hybrid plasma (ion kinetic) model (Lipatov et al., 2002) in three spatial dimensions (see, e.g. Lipatov and Combi, 2006) using a prescribed but adjustable neutral exosphere and the heavy ion clouds model for the Moon. A Boltzmann modeling is applied to model charge exchange between (incoming and pickup) ions and the immobile exospheric neutrals. In this paper we discuss the results of hybrid kinetic modeling of the lunar environment, namely, global plasma structures, e.g. the formation of the asymmetrical Mach cone, magnetic barrier, pickup ion tails etc. The results of this kinetic modeling are compared with the ARTEMIS flyby observational data. Comparison of results of our hybrid model with other ARTEMIS flybys will be presented in a future publication.
The paper is organized as follows: in Section 2 we present the computational model and a formulation of the problem. In Section 3 we present the results of modeling the plasma environment near the Moon and comparison with observational data. Finally, in Section 4 we summarize our results and discuss the future development of our computational model.
Section snippets
Formulation of the problem and mathematical model
To study the interaction of the solar wind with the ionized and neutral components of the lunar environment we use a quasi-neutral hybrid model, namely, a kinetic description for the upstream and pickup ions, and a fluid approximation for electrons. The hybrid model accurately describes wave-particle interactions on the following ion spatial () and time () scales: or and ; , where and denote the gyroradii for ions and electrons (respectively); is the
Results of the modeling
To study the interaction of the solar wind with the heavy ion cloud near the Moon the following sets of the magnetospheric plasma and exosphere parameters were adopted in accordance with flyby observational data: upstream velocity, densities and magnetic field: ; ; ; ; ; ; ; ; and . The effective Reynolds numbers inside the shell and core of the Moon are and . The
Conclusions
The hybrid modeling of Lunar plasma environment with ARTEMIS parameters with 3 ion species demonstrated several features:
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The light () and heavy () pickup ions form a multiple structured wake with external and internal tails.
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The pickup ions form a conducting layer near the day-side lunar surface and cause a compression in the magnetic field on the day-side of the lunar surface. The mass loading results in the formation of the asymmetrical Mach cone. The split structure of the Mach cone
Acknowledgments
A.S.L., J.F.C., E.C.S., and R.E.H were supported by the Grant Solar Wind Interaction with Lunar Exosphere and Surface (PI – J.F. Cooper) from the NASA NRA: Lunar Advance Science and Exploration Research Program (NNH08ZDA001-LASER). A.S.L. was also supported in part by the grant/task 670-90-315 between the GPHI/GEST Center UMBC and NASA GSFC. Computational resources were provided by the NASA Ames Advanced Supercomputing (NAS) Division (Project SMD-10-1517).
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