Neil Bowles: Publications

Fine Layering Effects on Thermal Infrared Emissivity of CI Simulant Materials

(2026)

Authors:

Emma-Catherine Belhadfa, Neil Bowles, Katherine Shirley

Abstract:

Introduction: Thermal infrared emissivity measurements of asteroid regolith analogs are challenging owing to atmospheric water vapor absorption, sample heating requirements, and the need for controlled atmospheric conditions [1], yet they provide fundamental constraints on surface thermal properties that cannot be obtained from reflectance spectroscopy alone [1]. While diffuse reflectance measurements have demonstrated that minimal fine dust coverage can dominate spectral signatures [2], spacecraft-based thermal emission instruments like the OSIRIS-REx Thermal Emission Spectrometer (OTES) observe different physical processes related to thermal emission rather than scattered light [3]. The disconnect between laboratory studies and spacecraft observations has thus limited our ability to interpret thermal infrared spectra of asteroid surfaces. Previous work using Space Resource Technology's CI simulant showed that 7-10 wt% fine dust coverage could impose fine-dominated reflectance features on coarse substrates [2], but the corresponding thermal emission properties remained uncharacterized. To bridge this gap, we conducted systematic thermal emissivity measurements of layered CI simulant materials using Oxford’s PASCALE instrument [4] under nitrogen atmosphere, constraining how dust deposition mechanisms affect the thermal emission processes observed by spacecraft instruments at airless bodies like asteroid (101955) Bennu. Methods: We measured thermal emission of layered CI simulant [5] samples using PASCALE under nitrogen atmosphere across 2000-400 cm⁻¹ (5-25 µm), eliminating atmospheric water vapor interference. Six layering configurations were tested, using 10 wt% fines (5% emissivity variations from unity), while the fluffy group shows more subdued but consistent spectral signatures. All method-dependent variations exceed the 2% measurement precision, demonstrating that dust deposition mechanism leaves diagnostic thermal emission signatures that can distinguish (and potentially identify) natural surface processes on airless body surfaces. Discussion: The separation between fluffy and compact layering methods demonstrates that thermal emission spectroscopy can distinguish surface formation processes on airless bodies. These results provide constraints missing from reflectance-only studies, by characterizing thermal emission properties relevant to spacecraft observations like OTES. The ability to spectrally distinguish between natural deposition processes offers new frameworks for understanding regolith evolution and thermophysical properties on asteroid surfaces. Summary: This study establishes thermal emissivity as a diagnostic tool for identifying dust deposition mechanisms on asteroid surfaces, demonstrating that layering processes leave distinct spectral signatures. References: [1] Salisbury et al. (1991) Icarus 92, 280-297. [2] Belhadfa et al. (2026) MaPs, In Prep. [3] Christensen P. R. et al. (2018) Space Science Reviews (Vol. 214, Issue 5). [4] Donaldson Hanna et al. (2019) Icarus 319, 701-723. [5] Landsman Z. et al. (2020) EPSC. 

Investigating the Detectability of Subsurface Lunar Water-Ice Beneath Regolith Dust Using Infrared Reflectance Spectroscopy

(2026)

Authors:

Fiona Henderson, Neil Bowles, Katherine Shirley, Jon Temple, Henry Eshbaugh

Abstract:

Hydration on the lunar surface has been widely identified in orbital datasets (e.g., M³, LCROSS, LAMP), yet the physical form, abundance, and spatial distribution of lunar volatiles remain poorly constrained. Interpretation is complicated by fine-grained regolith, which modifies local thermophysical conditions, obscures underlying volatiles, and alters diagnostic spectral features through scattering and photometric effects. These uncertainties are particularly significant for permanently shadowed regions (PSRs) and high latitudes , where temperatures below ~120 K may preserve water-ice over geological timescales and where several upcoming missions (e.g., Chang’e-7, PROSPECT, CLPS payloads, LEAP) aim to investigate insitu volatiles.We present the development of the Polar Analogue of Dust Overlying Regolith–Ice (PANDOR-I), a demountable laboratory vacuum chamber designed to simulate lunar polar conditions for infrared studies of water-ice and regolith mixtures. The system is engineered to operate under high vacuum and cryogenic conditions (~10⁻⁶ mbar; ≤120 K) and supports variable illumination geometries relevant to polar environments. PANDOR-I operates in two configurations: (1) coupled to a Bruker Vertex 70v FTIR spectrometer for laboratory reflectance measurements across 1.8–20 µm, and (2) integrated with existing flight-instrument thermal-vacuum facilities to enable direct observations by flight-ready infrared instruments.As an initial experimental phase prior to full cryogenic integration, the FTIR sample compartment has been isolated using KBr windows to enable controlled low-pressure (~0.2 mbar) reflectance measurements of hydrated and anhydrous regolith analogue configurations. These preliminary experiments investigate how dust layering, grain size, regolith maturity, composition, ice abundance, and mixing state influence the spectral expression of hydration features, with emphasis on the ~3 µm O–H stretching region and the diagnostic ~6 µm H–O–H bending mode of molecular water. Laboratory spectra will additionally be compared with Mie–Hapke forward models to examine band depth suppression, spectral mixing behaviour, and detectability thresholds under dusty polar conditions.This work reviews the laboratory framework for constraining infrared water-ice detection limits under mission-relevant lunar conditions and provides initial calibration datasets relevant to upcoming orbital and surface investigations of lunar polar volatiles.1. Honniball, C.I., Lucey, P.G., Hayne, P.O., Little, R.C., Greenhagen, B.T., Malespin, C. and Orlando, T.M., 2021. Molecular water detected on the sunlit Moon by SOFIA. Nature Astronomy, 5(2), pp.121–127. https://doi.org/10.1038/s41550-020-01222-x2. Saal, A.E., Hauri, E.H., Cascio, M.L., Van Orman, J.A., Rutherford, M.C. and Cooper, R.F., 2008. Volatile content of lunar volcanic glasses and the presence of water in the Moon’s interior. Nature, 454(7201), pp.192–195. https://doi.org/10.1038/nature070473. Buffo, J.J., Shepherd, J.D., Xu, J., Whisner, C., Devore, E., Shay, P. and Crites, S.T., 2025. Quantifying Regolith Cover Effects on 3 and 6 µm Water Ice Bands. 56th Lunar and Planetary Science Conference, Abstract 2152.4. Pieters, C.M., Goswami, J.N., Clark, R.N., Annadurai, M., Boardman, J., Buratti, B., Cheek, L., Dhingra, D.K., Green, R.O., Head, J.W., Hiesinger, H., Hypki, A., Isaacson, P., Jolliff, B.L., Klima, R.L., Kramer, G., Kumar, S., Lawrence, S.J., LeCorre, L., Li, S., Malaret, E., Mustard, J.F., Petro, N.E., Robinson, M.S., Samuelson, J., Sundaram, C.N. and Taylor, L.A., 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. https://doi.org/10.1126/science.11786585. McCord, T.B., Taylor, L.A., Combe, J.P., Klima, R.L., Tighe, R., Murray, K., Hayne, P.O., Clark, R.N., Pieters, C.M., Sunshine, J.M., Mellon, M.T., Hargraves, R.B., Dyar, M.D., Bussey, D.B.J., Paige, D.A. and Orlando, T.M., 2011. Sources and processes responsible for OH/H₂O in lunar soil. Journal of Geophysical Research: Planets, 116(E10). https://doi.org/10.1029/2010JE0037116. Ehlmann, B.L., Calvin, W.M., Bowles, N.E., Donaldson Hanna, K.L., Green, R.O., Greenhagen, B.T. and Shirley, K.A., 2022. Lunar Trailblazer: A pathfinding mission for lunar water and the lunar surface composition. IEEE Aerospace and Electronic Systems Magazine, 37(11), pp.6–22. https://doi.org/10.1109/AERO53065.2022.98436637. Bowles, N.E., Thomas, I.R., Calcutt, S.B., Donaldson Hanna, K.L., Ehlmann, B.L., Greenhagen, B.T. and Shirley, K.A., 2020. Lunar Thermal Mapper: Characterising the lunar surface in the mid-infrared. 51st Lunar and Planetary Science Conference, Abstract 1380.8. Colaprete, A., Schultz, P., Heldmann, J., Wooden, D., Ennico, K., Hermalyn, B., Marshall, W., Ricco, A., Shirley, M., Vergoz, J. and Yeomans, D., 2010. Detection of water in the LCROSS ejecta plume. Science, 330(6003), pp.463–468. https://doi.org/10.1126/science.11869869. Ogishima, A., Saiki, K., Okubo, A. and Sasaki, S., 2021. Development of a laboratory apparatus to reproduce lunar polar surface environment and measurements of reflectance spectra of frost on the regolith. Icarus, 358, 114192. https://doi.org/10.1016/j.icarus.2020.114192

MoonTools: A Framework for Hyperspectral Data Processing and Parameter Retrieval

(2026)

Authors:

Henry Eshbaugh, Katherine Shirley, Fiona Henderson, Namrah Habib, Emma Belhadfa, Robert Spry, Kevin Olsen, Neil Bowles

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.

Spectral–Mineralogical Correlations in Meteorite and Simulant Analogues: Implications for the Composition and Origin of Phobos

(2026)

Authors:

Emelia Branagan-Harris, Helena Bates, Katherine Shirley, Ashley King, Neil Bowles, Sara Russell

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

Stochastic Modelling of Volatile Transport in Surface-Bound Exospheres

(2026)

Authors:

Henry Eshbaugh, Neil Bowles

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.

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Neil Bowles

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Fine Layering Effects on Thermal Infrared Emissivity of CI Simulant Materials

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Investigating the Detectability of Subsurface Lunar Water-Ice Beneath Regolith Dust Using Infrared Reflectance Spectroscopy

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MoonTools: A Framework for Hyperspectral Data Processing and Parameter Retrieval

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Spectral–Mineralogical Correlations in Meteorite and Simulant Analogues: Implications for the Composition and Origin of Phobos

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Stochastic Modelling of Volatile Transport in Surface-Bound Exospheres

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Fine Layering Effects on Thermal Infrared Emissivity of CI Simulant Materials

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Investigating the Detectability of Subsurface Lunar Water-Ice Beneath Regolith Dust Using Infrared Reflectance Spectroscopy

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MoonTools: A Framework for Hyperspectral Data Processing and Parameter Retrieval

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Spectral–Mineralogical Correlations in Meteorite and Simulant Analogues: Implications for the Composition and Origin of Phobos

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Stochastic Modelling of Volatile Transport in Surface-Bound Exospheres

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