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Abstract:

MoonTools is a software framework, written in the Julia programming language [9], allowing straightforward, flexible, and performant processing of multispectral and hyperspectral data products. Designed originally to operate on M3 observations [4, 5], our framework is readily extensible to a wide range of datasets.Drawing from functional programming [6], our framework emphasizes composition of disparate operations. Processing pipelines are constructed in native Julia, parametrised by partial function application. This approach allows for flexibility of use and ease of extensibility, and distinguishes our work from similar tools, e.g. [7]; further, Julia’s just-in-time compilation  and parallel-programming tools allow for fast, multithreaded operations on multi-terabyte datasets, including for user-supplied inputs.Implemented operations include thermal and photometric corrections of multispectral radiance cubes, reflectance retrievals, spectral parameter determination, and post-processing amongst others. Additional utilities allow users to search datasets for targets by nomenclature, terrain type, and local solar time. Various dataset export options are available, including HDF5 products and “at a glance” views of regions of interest.We provide an example Julia pipeline in Listing 1, reproducing the detection of spinel at Theophilus crater [1,2]. We begin by importing the MoonTools package; then, we define a RATIO parameter expression. The spectral parameters SPINEL and PYROXENE are implemented as in [2] up to a constant factor using the RATIO definition. Invoked macros produce multithreaded CPU and GPU-kernel implementations of these parameters transparently to the user. Finally, a pipeline is composed: we search M3 data for observations of Theophilus crater, apply parameters, and produce “quicklook” plots of all matching observations; one such plot is shown in Figure 1.Listing 1: Pipeline invocation, including parameter definitions, required to produce Figure 1.using MoonTools@paramdef RATIO(λs, R; λ1, λ2) = sum(R[λ1]) / sum(R[λ2])@param SPINEL   RATIO [1400]       [1750]@param PYROXENE RATIO [0700, 1200] [0950]observations(:m3) > by_name("Theophilus") > PYROXENE > SPINEL > quicklookFigure 1: One of several quicklook outputs, showing Theophilus crater. Quicklooks are intended to provide overviews of regions of interest (RoIs) indicated by pipeline construction. Plots on the left include a reference narrowband reflectance, and PYROXENE and SPINEL parameter maps across the RoI. The RoI is partitioned into a 3x3 grid of zones; spectra sampled from each zone are plotted on the right in corresponding positions.Striping artifacts exist throughout the M3 dataset, and are prominent in spectral parameter products; state-of-the-art tooling must destripe these images [7,8]. We provide a bespoke destriping algorithm using a wavelet packet decomposition [3]. The modified pipeline is given in Listing 2; a destriped spinel map is shown in Figure 2.Listing 2: Pipeline altered from Listing 1; outputs are shown in Figure 2.observations(:m3) > by_name("Theophilus") > SPINEL > destripe!Figure 2: Destriped spinel parameter map. The before and after of the destriping operation are shown in the left and center plots; the removed signal is shown on the right.Software development is progressing rapidly. We anticipate a release of MoonTools to the scientific community in the coming months; MoonTools will be distributed under the terms of an open-source software license. We will welcome bug reports, feature requests, and contributions.References[1] Dhingra, D., Pieters, C.M., Boardman, J.W., Head, J.W., Isaacson, P.J. and Taylor, L.A., 2011. Compositional diversity at Theophilus Crater: Understanding the Geological Context of Mg‐Spinel-Bearing Central Peaks. Geophysical Research Letters, 38(11).[2] Pieters, C.M., Hanna, K.D., Cheek, L., Dhingra, D., Prissel, T., Jackson, C., Moriarty, D., Parman, S. and Taylor, L.A., 2014. The distribution of Mg-spinel across the Moon and constraints on crustal origin. American Mineralogist, 99(10), pp.1893-1910.[3] Mallat, S., 1999. A Wavelet Tour of Signal Processing. Elsevier.[4] Chandrayaan-1 Moon Mineralogy Mapper Science Team (2011). M3 L1B Gridded Spectral Radiance, Version 3. PDS Cartography and Imaging Sciences Node. https://doi.org/10.17189/1520248.[5] Chandrayaan-1 Moon Mineralogy Mapper Science Team (2011). L2 Gridded Spectral Reflectance (version 1) products. https://doi.org/10.17189/1520414.[6] Backus, J., 1978. Can Programming be Liberated from the von Neumann Style? A Functional Style and its Algebra of Programs. Communications of the ACM, 21(8), pp.613-641.[7] Suárez‐Valencia, J.E., Rossi, A.P., Zambon, F., Carli, C. and Nodjoumi, G., 2024. MoonIndex, an open‐source tool to generate spectral indexes for the moon from M3 data. Earth and Space Science, 11(6), p.e2023EA003464.[8] Shkuratov, Y., Surkov, Y., Ivanov, M., Korokhin, V., Kaydash, V., Videen, G., Pieters, C. and Stankevich, D., 2019. Improved Chandrayaan-1 M3 data: A northwest portion of the Aristarchus Plateau and contiguous maria. Icarus, 321, pp.34-49.[9] Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A., 2012. Julia: A Fast, Dynamic Language for Technical Computing. arXiv preprint arXiv:1209.5145.

Abstract:

Introduction: Phobos’ formation remains uncertain, with two main hypotheses: accretion of debris following a high-energy impact between Mars and an asteroid [1] or capture of a primitive asteroid [2]. To solve this, JAXA’s Martian Moons eXploration (MMX) mission aims to return samples from Phobos by 2031 [3]. The characterisation of these samples will determine the origin of Phobos.Current observations of Phobos are limited to remote measurements that are interpreted without direct mineralogical ground-truth. In this study, we have characterised the infrared (IR) reflectance spectra and mineralogy of meteorites considered good analogues for materials likely to be present on the surface of Phobos. These measurements provide a link between remote sensing data and physical sample analysis by building a spectral-mineralogical reference catalogue using powdered meteorites. This catalogue will help interpret the initial remote observations (prior to landing on Phobos’ surface) of the upcoming MMX mission, inform sampling site choices, and then help evaluate the later returned sample spectra to ultimately constrain the origin of Phobos. In addition, the mineralogical-spectral correlations can be referred to for future spectral calibration across other small bodies in the Solar System.Methods: We have characterised the mineralogy and spectral properties of six CM (Mighei-like) carbonaceous chondrites, Tarda (C2-ung), the CO (Ornans-like) chondrite Kainsaz, CRs (Renazzo-like) NWA 801 and 1567, a range of shock darkened ordinary chondrites (mostly falls) including L4-6 and H5-6, four ureilites, Martian meteorites Nakhla and Tissint (shergottite), and a Tagish Lake (C2-ung) based simulant created by the University of Tokyo, known as UTPS-TB [5].We performed FTIR and XRD measurements on the same powder (~50 mg, grain size

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:

Accurately characterizing the endogenic thermal signatures of icy moons requires a detailed understanding of surface heating driven by insolation, surface properties and local terrain. Due to the lack of high-resolution thermal observations, Europa’s surface temperatures have historically been sufficiently modelled using smooth surface or one-dimensional approximations [1]. However, Europa’s complex terrains alter local illumination conditions and surface temperatures. Here, we use TEMPEST, an open-source, python based thermophysical model [2, 3] to investigate the significance of radiative self-heating and light scattering on interpretation of Europa’s thermal IR emission, with a particular focus on detectability of near-surface liquid-water reservoirs by high spatial resolution (≤100 m/pixel) observations soon to be taken by Europa Clipper’s E-THEMIS instrument [4].Passive heating by solar illumination was modelled under Europa-like conditions using TEMPEST, which solves a surface energy balance that includes solar flux, thermal emission, vertical heat conduction, and (optionally) scattering, radiative self-heating and macroscopic surface roughness. The 1D periodic conduction solver at its core is based on thermprojrs [5]. Comparing a smooth sphere (fig. 1a, c) to the topographic digital elevation model (DEM) (fig. 1b, d) reveals that while mean temperatures remain comparable, topography introduces substantial heterogeneity. For example, the DEM has a wider distribution of surface temperatures due to local slope and shadowing effects. Thermal radiance scales with Temperature4 (Stefan-Boltzmann law), so the small minority of hotter regions can dominate the thermal IR signal (fig. 2). We show that rough terrain emits significantly higher total radiance than a smooth sphere despite the similar mean kinetic temperature.Simulated topographic temperature maps show that light scattering and radiative exchange with local terrain cause increased surface temperature heterogeneity, with local temperature increases in deep fractures as high as 35 K (fig. 3). Temperature heterogeneity can systematically bias mean temperature measurements, and localised mutual radiative heating can lead to apparently anomalous warm regions, potentially mimicking endogenic heating, or obscuring indications of conductive heating from near-surface water reservoirs. To place these effects in the context of E-THEMIS detectability, we will use first-order conductive heat-flux calculations to estimate the surface expressions of idealised subsurface liquid-water reservoirs at different depths and spatial scales. These calculations will provide test cases for distinguishing plausible endogenic thermal anomalies from topographically induced radiance variations. This work shows that if topography is ignored, the excess radiance caused by it could be misinterpreted as a region of lower thermal inertia (in daytime), or even an endogenic heat source (hotspot). Accurately modelling terrain reduces the risk of false positives when searching for plume sources or active regions.Figure 1: Surface temperature maps for a section of an icy moon modelled as a smooth sphere (a) and using a digital elevation model (DEM) of Enceladus (b) using Europa thermal parameters. Histograms (c) and (d) show the distribution of surface temperatures within the above terrain samples.   Figure 2: The integrated surface radiance from each terrain sample. The T4 dependence of radiance leads to significantly higher emission from the DEM terrain.Figure 3: Simulated Europa surface temperatures without (a) and with (b) multiple scattering and radiative self-heating. Panel (c) shows the resulting temperature difference, with local increases up to 35 K in shadowed terrain. Enceladus DEM [6] provides an icy-terrain analogue for topographic heating effects.  Future work will repeat this analysis using Europa-specific topography where available. References:[1] Rathbun, J. A., Rodriguez, N. J., & Spencer, J. R. (2010). Galileo PPR observations of Europa: Hotspot detection limits and surface thermal properties. Icarus, 210(2), 763-769.[2] Lyster, D., Howett, C., & Penn, J. (2025). TEMPEST: A Modular Thermophysical Model for Airless Bodies with Support for Surface Roughness and Non-Periodic Heating. EPSC-DPS 2025, 1479.[3] Chivers, C.J., Hayne, P.O. and Schmidt, B.E. (2025). Prospects For Detecting Shallow Liquid Water Bodies At Europa Using E-Themis. LPSC 2025, 1226.[4] Christensen, P.R., Spencer, J.R., Mehall, G.L., Patel, M., Anwar, S., Brick, M., Bowles, H., Farkas, Z., Fisher, T., Gjellum, D. and Holmes, A. (2024). The Europa thermal emission imaging system (E-THEMIS) investigation for the Europa clipper mission. Space Science Reviews, 220(4), 38.[5] Spencer, J.R., Lebofsky, L.A., and Sykes, M.V. (1989). Systematic biases in radiometric diameter determinations. Icarus, 78(2), 337-354. [6] Park, R.S., Mastrodemos, N., Jacobson, R.A., Berne, A., Vaughan, A.T., Hemingway, D.J., Leonard, E.J., Castillo-Rogez, J.C., Cockell, C.S., Keane, J.T. and Konopliv, A.S. (2024). The global shape, gravity field, and libration of Enceladus. Journal of Geophysical Research: Planets, 129(1), e2023JE008054.

Abstract:

Thermal measurements provide key constraints on the physical properties of icy satellite surfaces, including grain size, porosity, and regolith structure. On the icy moon of Jupiter Europa, previous analyses [e.g., 1,2] of the Galileo Photopolarimeter–Radiometer (PPR) dataset revealed heterogeneities in thermal inertia, but the limited spatial resolution and coverage prevented a detailed characterization of the thermophysical properties of the surface. Yet, the determination of these thermophysical properties is crucial to predict the surface temperatures of Europa that can be used to search for endogenic activity as hot spots, one of the main scientific targets of the upcoming NASA’s Europa Clipper mission [3].We derived high-resolution maps of Europa’s surface albedo and thermal inertia, and inferred the microphysical properties of its icy regolith, through a reanalysis of the Galileo PPR dataset. We specifically investigated the spatial variability of these properties to discuss the processes controlling the thermophysical evolution of Europa’s surface. To do so, we used the KRC thermal model [4] to analyze the PPR brightness temperatures and retrieve the albedo and thermal inertia that best fit the observations. These values were then interpreted using theoretical conductivity models of porous ice [5] to constrain grain size and porosity and to investigate possible sintering processes affecting the surface.We will present at the conference our main results: we derived a mean Bond albedo of 0.64 ± 0.06 (standard deviation of 1σ) and a mean thermal inertia of 56 ± 17 J m−2 K−1 s−1/2 (1σ). The thermal inertia shows significant spatial variations, including a band of low thermal inertia at the equator (39 ± 7 J m−2 K−1 s−1/2, 1σ) and higher values (56 ± 11 J m−2 K−1 s−1/2, 1σ) at mid-latitudes on the leading hemisphere (0°–180° W). The equatorial region of the trailing hemisphere (180° W–360° W) also exhibits higher thermal inertia (63 ± 17 J m−2 K−1 s−1/2, 1σ) than the leading hemisphere, likely related to compositional differences. Interpreting the thermal inertia with conductivity models indicates a porous icy regolith with grain sizes ranging from a few micrometers to a few centimeters and an average porosity of 0.61 ± 0.1 (1σ).Interestingly, the thermal inertia distribution shows little correlation with geological units, the Pwyll ejecta being a notable exception, with markedly higher values than the surrounding terrain. In contrast, the good agreement between the thermal inertia distribution and modeled sputtering rates suggests that sputtering-driven sintering may play a fundamental role in controlling the thermophysical properties of Europa’s surface. The absence of a high thermal inertia equatorial band analogous to the PacMan anomaly observed on Saturn’s icy moons [e.g., 6] indicates that electron-driven sintering is inefficient on Europa, while temperature-gradient metamorphism may instead enhance grain growth at depth, potentially explaining the absence of large grains at the surface. In addition, modeled surface temperatures range from ~67 to 148 K at mid to low latitudes, with peak daytime temperatures counteracting radiolytic amorphization, while limiting the stability of volatile species. During the Europa Clipper mission (2031–2034), temperatures are expected to be slightly lower, ranging between 67.6–141.2 K. Our predictions provide a framework for interpreting future observations by the Europa Thermal Emission Imaging System (E-THEMIS) onboard Europa Clipper and the Submillimetre Wave Instrument (SWI) on JUICE. These future thermal measurements will provide key constraints to test these hypotheses and refine our understanding of the evolution of Europa’s icy regolith as well as search for active hot spots.AcknowledgementsLL’s research was supported by an appointment to the NASA Postdoctoral Program administered by Oak Ridge Associated Universities at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). Part of this work was performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). Some of the computational analyses were run on Northern Arizona University’s Monsoon computing cluster, funded by Arizona’s Technology and Research Initiative Fund. © 2026. All rights reserved.References[1] Rathbun et al., 2010, Icarus, 210, 763–769[2] Rathbun & Spencer, 2020, Icarus, 338, 11350[3] Pappalardo, R. T., Buratti, B. J., Korth, H., et al. 2024, SSR, 220[4] Kieffer, H. H. 2013, JGR: Planets, 118, 451–470[5] Ferrari & Lucas, 2016, A&A, 588, A133[6] Howett et al., 2011, Icarus, 216, 221–22