Neil Bowles

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.  

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:

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:

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