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Abstract:

The discovery of widespread hydration across the lunar surface [1,2,3] is one of the most surprising results of the last twenty years in the field of planetary science. Further considerations have extended to exospheric migration of water on other airless bodies, such as Mercury and Ceres [4]; other works have considered the coupling of multiple volatile species within the lunar volatile inventory [5,6], or have considered the icy moons of the outer Solar System [7], with the seasonal migration of CO2 on the Uranian moon Ariel [8,13] being one such example.A difficulty in modelling volatile transport is the interplay between various physical and chemical processes, including adsorption kinetics, photochemistry, and transport kinematics. Hence, Monte Carlo modelling has remained the dominant mode of investigation, producing qualitative outputs surveying emergent phenomena. The development of a comprehensive and quantitative forward-modelling approach has remained an outstanding problem.To produce such a model, we turn to Markov processes [9]. The governing equations of these processes generate, via Kramers-Moyal expansion [10], such familiar results as the Fokker-Planck, continuity, and diffusion equations.We derive a Markov master equation for a global volatile ensemble from mass-balance. With straightforward probability theory, we model adsorption kinetics at a molecular level, and ballistic transport on a global scale, providing an integrated analytic approach to global volatile dynamics.Going further, we use tensor products between Markov processes [11,12] to model the interplay between volatile species, allowing for capture of kinetic schemes driven by surficial and photochemical reactions.We implement a simplified, straightforward model. The global ensemble was taken to be 10^30 water molecules. 4200 timesteps per lunation are calculated for 100,000 timesteps - approximately 607 seconds per timestep. We neglect implantation and loss mechanisms as well as topography. We use an analytic model of lunar surface temperatures [14]. Desorption probabilities are calculated from the Eyring-Polanyi equation [15,16], with an activation energy of 0.7 eV. The Armand distribution [17,18] drives ballistic-hop transport.Figure 1: Lunar surficial water abundance; simplified model run, timestep 8200.Timestep 8200 is shown in Figure 1. Volatile concentration is heightened in the southern winter. The initial volatile distribution was random; onset of equilibrium conditions is rapid. A dusk-dawn asymmetry is present, reproducing the results of Schörghofer [19]. Latitudinal stratification is visible, with the band of minimal concentration dependent on solar declination. We conclude by presenting paths forward in volatile modelling efforts enabled by this approach. [1] Pieters, C.M., Goswami, J.N., Clark, R.N., Annadurai, M., Boardman, J., Buratti, B., Combe, J.P., Dyar, M.D., Green, R., Head, J.W. and Hibbitts, C., 2009. Character and spatial distribution of OH/H2O on the surface of the Moon seen by M3 on Chandrayaan-1. science, 326(5952), pp.568-572.[2] Sunshine, J.M., Farnham, T.L., Feaga, L.M., Groussin, O., Merlin, F., Milliken, R.E. and A’Hearn, M.F., 2009. Temporal and spatial variability of lunar hydration as observed by the Deep Impact spacecraft. Science, 326(5952), pp.565-568.[3] Clark, R.N., 2009. Detection of adsorbed water and hydroxyl on the Moon. Science, 326(5952), pp.562-564.[4] Schörghofer, N., Benna, M., Berezhnoy, A.A., Greenhagen, B., Jones, B.M., Li, S., Orlando, T.M., Prem, P., Tucker, O.J. and Wöhler, C., 2021. Water group exospheres and surface interactions on the Moon, Mercury, and Ceres. Space Science Reviews, 217(6), p.74.[5] Huebner, W.F. and Mukherjee, J., 2015. Photoionization and photodissociation rates in solar and blackbody radiation fields. Planetary and Space Science, 106, pp.11-45.[6] Smolka, A., Nikolić, D., Gscheidle, C. and Reiss, P., 2023. Coupled H, H2, OH, and H2O lunar exosphere simulation framework and impacts of conversion reactions. Icarus, 397.[7] Steckloff, J.K., Goldstein, D., Trafton, L., Varghese, P. and Prem, P., 2022. Exosphere-mediated migration of volatile species on airless bodies across the solar system. Icarus, 384, p.115092.[8] Cartwright, R.J., Nordheim, T.A., DeColibus, R.A., Grundy, W.M., Holler, B.J., Beddingfield, C.B., Sori, M.M., Lucas, M.P., Elder, C.M., Regoli, L.H. and Cruikshank, D.P., 2022. A CO2 Cycle on Ariel? Radiolytic production and migration to low-latitude cold traps. The Planetary Science Journal, 3(1), p.8.[9] Livi, R. and Politi, P., 2025. Nonequilibrium Statistical Physics: a Modern Perspective. 2nd edition. Cambridge University Press.[10] Kramers, H.A., 1940. Brownian motion in a field of force and the diffusion model of chemical reactions. physica, 7(4), pp.284-304.[11] Dayar, T., 2012. Analyzing Markov chains using Kronecker products: theory and applications. Springer Science & Business Media.[12] Giry, M., 2006, October. A categorical approach to probability theory. In Categorical Aspects of Topology and Analysis: Proceedings of an International Conference Held at Carleton University, Ottawa, August 11–15, 1981 (pp. 68-85). Berlin, Heidelberg: Springer Berlin Heidelberg.[13] Grundy, W.M., Young, L.A., Spencer, J.R., Johnson, R.E., Young, E.F. and Buie, M.W., 2006. Distributions of H2O and CO2 ices on Ariel, Umbriel, Titania, and Oberon from IRTF/SpeX observations. Icarus, 184(2), pp.543-555.[14] Crider, D.H. and Vondrak, R.R., 2000. The solar wind as a possible source of lunar polar hydrogen deposits. Journal of Geophysical Research: Planets, 105(E11), pp.26773-26782.[15] Eyring, H., 1935. The activated complex in chemical reactions. The Journal of chemical physics, 3(2), pp.107-115.[16] Evans, M.G. and Polanyi, M., 1935. Some applications of the transition state method to the calculation of reaction velocities, especially in solution. Transactions of the Faraday Society, 31, pp.875-894.[17] Armand, G., 1977. Classical theory of desorption rate velocity distribution of desorbed atoms; possibility of a compensation effect. Surface Science, 66(1), pp.321-345.[18] Schörghofer, N., 2022. Statistical thermodynamics of surface-bounded exospheres. Earth, Moon, and Planets, 126(2), p.5.[19] Schorghofer, N., 2014. Migration calculations for water in the exosphere of the Moon: Dusk‐dawn asymmetry, heterogeneous trapping, and D/H fractionation. Geophysical Research Letters, 41(14), pp.4888-4893.

Abstract:

Remote spectroscopy is used to characterize the mineralogy and infer the history of planetary bodies. Carbonaceous asteroids, such as B‐type (101955) Bennu, represent the earliest stages of planet formation. B types have a blue (negative) spectral slope and comprise <5% of asteroids. Samples from Bennu returned by the OSIRIS‐REx spacecraft complement remote observations of this rare population. We show here, using laboratory spectra that are directly comparable to spacecraft data, that OSIRIS‐REx accurately determined Bennu's dust content and most of its surface composition. However, spectra of the asteroid exhibit stronger water absorptions than those of bulk samples, possibly due to hydrous, Mg‐rich phosphate or solar wind implantation at Bennu's uppermost surface. Bennu samples spectrally resemble the most aqueously altered carbonaceous meteorites and samples of (162173) Ryugu, indicating similarly pervasive aqueous alteration. However, one carbon‐enriched Bennu stone does not appear to have a spectral analog among Ryugu samples or meteorites. Our findings demonstrate the leverage obtained using a wide range of wavelengths and that sample analysis anchors the interpretations of remote sensing, leading to more robust characterization of planetary surface composition and evolution.

Abstract:

Plain Language Summary: We present laboratory tests of a new instrument, the High‐resolution Volatiles and Minerals Moon Mapper ( H V M 3 ${\mathrm{H}\mathrm{V}\mathrm{M}}^{3}$ ). H V M 3 ${\mathrm{H}\mathrm{V}\mathrm{M}}^{3}$ will launch onboard the National Aeronautics and Space Administration's Lunar Trailblazer mission. It is an imaging spectrometer that will measure the spectrum of reflected sunlight in visible and infrared wavelengths for every pixel of an image. Scientists will interpret these spectra with the goal of measuring how water and ice are distributed across the lunar surface. Our laboratory tests indicate that the instrument is sensitive enough to measure small changes in the content of water on the lunar surface. We find the instrument capable of measuring areas of the Moon that are in permanent shadow using light reflected from neighboring surfaces like crater walls.

Abstract:

The Lunar Trailblazer mission launched in February of 2025 with the goal of characterizing lunar surface water through a targeted campaign. One instrument on the mission, the Lunar Thermal Mapper (LTM), was tasked with measuring the surface temperature to compare with maps of the form and abundance of water on the lunar surface. LTM's secondary science goals were to identify regolith composition and thermophysical properties as exhibited by mid‐infrared spectral features. Here we show the utility of LTM in distinguishing lunar regolith composition with its 11 narrow bands. Five spectral parameter products were developed to aid in early identification of regions of interest for follow‐on spectral analyses. These products include the Christiansen feature (CF) value, weighted absorption center (WAC) value, WAC band depth, Transparency Roll‐off, and a Diviner CF value equivalent. These products would be used mainly to flag these regions for more detailed follow‐up study with the entire spectral capabilities of the mission instrumentation. The Lunar Thermal Mapper (LTM) is one of two instruments on the Lunar Trailblazer mission launched in February 2025. LTM's primary goal is to provide surface temperature measurements for the lunar surface, in particular for identifying and mapping water on the Moon. LTM is also capable of identifying the compositional and physical properties of different rocks on the surface. Here, we test those capabilities and determine five methods for quickly distinguishing bulk properties of the lunar rocks that can be used by the community to identify regions of interest for further investigation. Mid‐infrared compositional parameters were created and tested for the Lunar Trailblazer mission Spectral parameters can distinguish bulk silicate mineralogy, and identify regions of compositional interest The Christiansen feature roll‐off parameter can provide an initial identification of areas with distinct thermophysical properties Mid‐infrared compositional parameters were created and tested for the Lunar Trailblazer mission Spectral parameters can distinguish bulk silicate mineralogy, and identify regions of compositional interest The Christiansen feature roll‐off parameter can provide an initial identification of areas with distinct thermophysical properties

Abstract:

The Lunar Trailblazer mission aimed to assess the presence of water on the lunar surface using imaging spectroscopy in visible shortwave infrared (VSWIR) coupled with high‐resolution multispectral imaging in thermal midwave‐infrared (MWIR), captured simultaneously over the same target from orbit around the Moon with two different instruments. Uncertainties in clock timing, instrument models, and instrument pointing knowledge manifest as geospatial offsets between the two data sets that must be corrected in post‐processing to enable co‐registration, tying the acquired images to their precise latitudes and longitudes on the Moon. This work describes an algorithmic approach to co‐registering and geolocalizing images after acquisition without high precision instrument and spacecraft pointing models, the Iterative Matching Pipeline for Post‐Acquisition Image Localization (IMPPAIL), utilizing previously acquired data for development. We use Lunar Orbiter Laser Altimetry (LOLA) and Kaguya data to make shaded relief maps as the basemap on which to project data. To test our processing pipeline prior to Lunar Trailblazer data collection, we use Moon Mineralogy Mapper (M3) data for VSWIR images and simulated MWIR images. When demonstrated on these data sets, IMPPAIL produces a 98% success rate registering VSWIR data to LOLA/Kaguya shaded relief maps and successfully co‐registered MWIR and VSWIR in all four simulation cases. We include a code package with software tools allowing this algorithm to be used for a variety of data sets across many other missions. The Lunar Trailblazer spacecraft was designed to map surface temperature and water content on the Moon via satellite imagery. Onboard are two imagers that measure thermal and visible light bands, which must be aligned to be used in conjunction with each other as well as located precisely relative to pre‐existing topography data. To do this, we develop an algorithm which iteratively matches key features between the images, allowing for the co‐registration of data points. We test the algorithm on topography and visible imagery data from a past satellite mission and simulated data for thermal images. We show 98% accuracy and success in both imagery wavelength bands. While this was originally created for use on the Lunar Trailblazer mission, the process is applicable to future missions requiring similar functionality, and we make the code available for other users. We present a process for post‐acquisition, image‐based data localization for satellite missions with use for the Lunar Trailblazer mission We demonstrate image matching success across short‐ and mid‐wave infrared image wavelength bands and topographic hillshade data sets This IMPPAIL procedure is applicable to future missions that require higher pointing accuracy than is possible with hardware solutions We present a process for post‐acquisition, image‐based data localization for satellite missions with use for the Lunar Trailblazer mission We demonstrate image matching success across short‐ and mid‐wave infrared image wavelength bands and topographic hillshade data sets This IMPPAIL procedure is applicable to future missions that require higher pointing accuracy than is possible with hardware solutions