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Abstract:

Introduction: The origins of Mars' moons, Phobos and Deimos, remain uncertain, with two main hypotheses under consideration: formation from debris following a high-energy impact between Mars and an asteroid [1], or capture of primitive asteroids [2]. To address 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.To ground-truth remote observations of Phobos, we have used X-ray diffraction (XRD), and Fourier transform infrared (FTIR) reflectance spectroscopy to characterise the bulk mineralogy and IR spectral properties of ureilites, carbonaceous and ordinary chondrites, the composition of which could be indicative of a captured asteroid [4], and Martian meteorites that could represent a collisional formation. By acquiring XRD and IR data from the same material, mineral abundances can be directly correlated with features in reflectance spectra [5]. When MMX reaches Phobos, meteorite data collected in the laboratory will play a crucial role towards interpreting the mineralogy and composition of materials on its surface.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, a range of shock darkened ordinary chondrites (mostly falls) including L4-6, and H5-6, four CR2 chondrites, four ureilites, Martian meteorites Nakhla and Tissint, and a Tagish Lake (C2-ung) based simulant created by the University of Tokyo, known as UTPS-TB [6]. For the meteorites, chips of approximately 200 mg were ground to produce powders with grain sizes of less than 40 microns. The UTPS-TB sample came in a powdered form which was ground to the same grain size as the meteorites.Diffuse reflectance spectra (1.7 - 50 μm) were collected using a Bruker VERTEX 70V FTIR spectrometer at the 91̽�� Planetary Spectroscopy Facility. Spectra were calibrated at the start of each measurement day and between measurements of samples using a gold standard. The powdered sample was measured under a vacuum to reduce terrestrial atmospheric contributions.XRD patterns of the same powders were collected using an INEL X-ray diffractometer with a position-sensitive detector at the Natural History Museum, London. Around 50 mg of powdered sample was measured for 16 hours to achieve good signal-to-noise. Measurements of well-characterised standard minerals were collected for 30 minutes and compared with meteorite patterns to identify minerals and quantify their abundance in the sample [e.g. 7].Results & Discussion: The mineralogical and spectral characteristics of meteorites in this investigation are compared the reflectance spectra of Phobos’ surface. The CR chondrites are primitive, containing both anhydrous silicates (e.g. olivine and pyroxene) and aqueous alteration phases such as phyllosilicates, carbonates, magnetite, and sulfides. Their albedo is ~3-5% reflectance with a weak red slope in the visible to near-infrared (VNIR). The CRs have a 3 μm hydration band, due to partial aqueous alteration. Their low VNIR reflectance, red-sloped continuum, and weak 3 μm spectral absorption feature is like that of Phobos, 91̽��ing the captured asteroid origin theory. The CM chondrites share similar spectral features but have a lower albedo and a stronger μm hydration band, corresponding to a higher phyllosilicate composition.   The ureilites are achondritic ultramafic meteorites containing olivine, pyroxene and carbon phases. These samples have a low albedo (~6-15% in VNIR) due to their opaque carbonaceous composition. However, their VNIR spectra are blue-sloped, inconsistent with Phobos’ red-sloped spectra. Ureilites are also anhydrous and therefore lack the 3 μm hydration band seen in Phobos spectra. Their low reflectance and feature-poor spectra could resemble Phobos, however there is a significant difference in spectral slope and hydration features. Therefore, Phobos were composed of ureilitic material, its surface would need to be significantly modified by space weathering.Martian meteorites Nakhla (a nakhlite) and Tissint (a shergottite) have mineralogical and spectral features consistent with their basaltic origin. XRD measurements of these meteorites are dominated by pyroxene (augite, pigeonite), and olivine, consistent with their origin in the Martian crust. Their reflectance spectra have relatively high albedo, mafic absorption bands at ~1 and 2 μm, and a lack of hydration features. These features are inconsistent with the spectra of Phobos, which lack 1 or 2 μm bands and show significantly lower reflectance.CR and CM chondrites are the closest spectral match to Phobos from the samples studied. Their low albedo, red-sloped, hydrated spectra are consistent with surface measurements of Phobos. Ureilites share low reflectance but differ significantly in slope and hydration, while Martian meteorites differ in more spectral characteristics. These results 91̽�� the interpretation that Phobos is composed of primitive, carbon-rich material, likely of outer solar system origin, and favour a capture scenario over a collisional formation from Martian ejecta. The similarities between the carbonaceous chondrites and Phobos indicates that the Martian moons may be captured asteroids and further demonstrates the importance of the MMX mission sample return for solving the mystery of their origin definitively.References: [1] R. Citron et al. (2015) Icarus 252:334-338. [2] M. Pajola et al. (2013) The Astrophysical Journal 777:127. [3] K. Kuramoto et al. (2022) Earth, Planets and Space 74:12. [4] K. D. Pang et al. (1978) Science 199(4324):64-66. [5] H. C. Bates et al. (2023) Meteoritics & Planetary Science 1-23. [6] H. Miyamoto et al. (2021) Earth, Planets and Space 73:1-17 [7] G. Cressey et al. (1996) Powder Diffraction 11:35-39.

Abstract:

Introduction: The Lunar Thermal Mapper (LTM) is a multispectral infrared radiometer, built by the 91̽�� Physics Instrumentation Group for the Lunar Trailblazer mission; a small satellite launched in February 2025 under NASA’s Small Innovative Missions for Planetary Exploration (SIMPLEx). Trailblazer aims to advance our understanding of the lunar water cycle by mapping surface temperature, water abundance, distribution and form (OH, H2O, ice) and silicate lithology (i.e., Si-O Christiansen spectral feature). LTM was developed to improve upon existing infrared instrumentation in lunar orbit (e.g., Diviner Lunar Radiometer Experiment, hereafter referred to as Diviner) to provide higher resolution temperature estimations and refine interpretations of thermophysical properties at the surface [2, 3]. Accurately determining surface temperatures on airless bodies is essential for deriving emissivity spectral features (such as the Christiansen Feature and Restrahlen bands, which are diagnostic of silicate lithologies) that are representative of the surface. Temperature errors can affect spectral shape, resulting in the misidentification of surface composition [5, 8].  Our team compared six methods for estimating LTM’s brightness temperature (BT), including the temperature retrieval approach used by Diviner, to (1) determine which method provides the most representative surface temperature and (2) assess how variations in BT estimation affect derived emissivity spectral shape. Despite challenges facing the Trailblazer mission, refining methods for BT estimation remains relevant to the planetary community, as future missions continue to depend on infrared instrumentation and accurate BT retrievals for remote compositional interpretation (e.g., LEAP, L-CIRiS, Europa Clipper).   Instrumentation: LTM is a 15-channel infrared imager that covers a range between 6 to 100 µm [2,3]. LTM advances infrared compositional analysis by incorporating eleven narrowband compositional filters across the 6.25 to 10 µm range. This expanded spectral coverage enables more precise characterization of key features, such as the Christiansen Feature, Reststrahlen bands, and transparency features, which are essential for identifying spectral endmembers (Table 1) [2,3].   LTM builds upon Diviner, a nine-channel instrument that has a broad spectral range from 0.3 to 400 µm (Table 1) [1]. Diviner’s three narrowband compositional channels, 7.55–8.05 µm (Channel 3), 8.10–8.40 µm (Channel 4), and 8.38–8.60 µm (Channel 5), are specifically tuned to capture the Christiansen Feature (CF), an emissivity peak that is diagnostic of broad silicate mineralogy and sensitive to variations in silica content [1,4].  Table 1: LTM and Diviner observational parameters.    Methodology: To assess BT and emissivity retrieval techniques for LTM, we measured four lunar analog samples under controlled laboratory conditions to retrieve high-resolution emission spectra. These laboratory spectra were down-sampled to match LTM’s narrowband spectral resolution. Six BT estimation methods were tested to determine how effectively each method preserved laboratory spectral shape and temperature. The following section describes the laboratory setup and the BT estimation methods examined in this study. Laboratory: Using the PASCALE (Planetary Analogue Surface Chamber for Asteroids and Lunar Environments) in conjunction with a Bruker 70V Fourier Transform Infrared (FTIR) spectrometer, we conducted thermal infrared measurements of four volcanic lunar analogue samples; dunite (Twin Sisters -1 and -2), basalt (BIR-1) and rhyolite (RGM-1) under controlled ambient conditions (350 K, 1000 mbar, N2 atmosphere) [4]. The integration of PASCALE with FTIR allows for the acquisition of thermal emission spectra (as opposed to typical laboratory reflectance), offering a more representative analog of data collected by orbiting infrared instrumentation. Spectra were measured across ~6000 to 350 cm⁻¹ at a resolution of 4 cm⁻¹. Quality assurance and calibration procedures followed established protocols outlined in [6,7,8].  BT Estimations: To evaluate BT performance at LTM’s spectral resolution, each sample’s measured radiance was convolved with LTM’s filter response to simulate instrument-resolution radiance. The resulting spectra were converted to BT using the Planck function. Seven distinct methods were applied to the LTM-resolution BT data to determine the maximum BT values for each sample (Table 2). Emissivity was subsequently derived as the ratio between the observed LTM-resolution radiance and an ideal blackbody at the retrieved maximum BT for each method across all samples. The accuracy of the BT estimation methods was assessed by comparing the resulting emissivity spectra and maximum BT values to the full laboratory reference data (350K and full resolution emissivity). Additionally, a focused comparison with Diviner’s BT retrieval method was conducted to identify method-specific discrepancies and evaluate cross-instrument consistency.Table 2: BT estimation methods Results & Discussion: Six BT estimation methods were applied to laboratory emissivity spectra of four lunar analogue samples (dunite, basalt, and rhyolite), as shown in Figure 2. The associated standard errors (SE) for each method are reported in Table 3. Among the tested approaches, four methods (3rd degree polynomial, quadratic, spline and narrowband maximum) showed close agreement with high-resolution laboratory spectra (Figure 2). Temperature variations across compositions were minor, with low SE values (Table 3).  Since the spline fit did not significantly outperform the simpler polynomial or narrowband methods, lower complexity approaches are preferred for LTM temperature retrievals, with a maximum SE of 3.42%.In contrast, due to limited spectral sampling, the Diviner method underestimates surface temperatures by up to 19 K (SE max: 5.55%) in the dunite (TS-2) sample. Expanding this analysis to include a broader range of lithologies or impacted processed samples would help assess whether the Diviner approach (and potential other methods with sparse spectral sampling) introduce systematic shifts in the Christiansen Feature (CF) position or affect the spectral shape relative to more spectrally resolved techniques.  Table 3: BT estimations and associated SE of temperature for dunite (TS-1, TS-2), basalt (BIR-1) and rhyolite (RGM-1). Fig 2: Six BT methods are fitted to laboratory emissivity spectra of four lunar analogues. Conclusion:Comparisons between BT estimation methods indicate the 3rd-degree polynomial, quadratic, and narrowband maximum methods offer the best agreement with laboratory data (SE max: 3.42%). Although Diviner’s method tends to underestimate surface temperatures (up to 19 K), it still preserves spectral shape and wavelength range, 91̽��ing the reliability of compositional interpretations. Expanding the dataset to include a broader range of compositions could confirm whether different approaches result in systematic shifts in the Christiansen Feature across different lithologies. This work enhances the accuracy of remote compositional interpretation and 91̽��s future exploration on airless bodies.

Abstract:

Introduction: The surfaces of airless bodies, such as asteroid (101955) Bennu, are typically composed of a regolith mixture containing both coarse and fine particulates. Observations from NASA’s Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) mission demonstrated a discontinuity between the remote sensing derived thermophysical properties and thermal spectroscopy results, indicating that a fine layer of dust may be coating the large boulders and coarse regolith surface [1]. To better understand the impact of such a coating on the thermal infrared spectra measured at Bennu, this work developed experimental methods for simulating dust coverings using Space Resource Technology’s CI simulant, based on the bulk composition of the Orgueil meteorite [2].    Figure 1: FTIR Reflectance Spectra of Control Samples of CI simulant. Figure 2: Figure 2: Microscope Camera Images of Sample Surfaces (7%, 10%, 15%, 20%, 25%, 50% Fines wt) of CI simulant Methods: The CI simulant was first sieved into seven size fractions: 1000 mm. An unsieved sample was used as a control. The spectra of the eight samples were measured using a Bruker Vertex 70v Fourier Transform Infrared Reflectance (FTIR) spectrometer, normalized using a gold standard prior to and after measurements, in the range of 1000-650 cm-1 (Figure 1). The dust coating was simulated by placing increasing mass fractions of fine particulates (10% change in the spectral slope).  Implications for OSIRIS-Rex Findings: From the data returned by the OSIRIS-Rex Thermal Emission Spectrometer (OTES) [3],  thermal inertia modelling imply that the surface is porous;, however, the spectral findings indicate that the surface is composed of non-porous? rocks with thin dust coatings [4]. Our experiments find that as little as ~7 wt % of 7% wt) was sufficient to overwhelm and dominate mid-infrared emissivity spectra. The results indicate that the discontinuity in OTES data could be linked back to dust coating on the larger rocks and boulders.   References: [1] Tinker C. et al. (2023) RAS Techniques and Instruments (Vol. 2, Issue 1). [2] Landsman Z. et al. (2020) EPSC 2020. [3] Christensen P. R. et al. (2018) Space Science Reviews (Vol. 214, Issue 5). [4] Rozitis B. et al. (2022) JGR: Planets (Vol. 127, Issue 6). [5] Rivera-Hernandez F. et al. (2015) Icarus (Vol. 262). 

Abstract:

Introduction: During the Reconnaissance phase of NASA’s OSIRIS-REx mission, the Thermal Emission Spectrometer (OTES) acquired high–spatial resolution emissivity spectra over Bennu’s four prospective sampling sites [1, 2]. We analyse the calibrated OTES dataset archived in the Planetary Data System [3] to quantify compositional and mineralogical diversity across the original four candidate sample sites (Nightingale, Kingfisher, Osprey, and Sandpiper) and to explore possible drivers of Bennu’s surface heterogeneity, including implications for Bennu’s mineralogy and space-weathering history.  Figure 1: Site-Averaged Emissivity Spectra with Annotated Band Parameters Methods: Calibrated emissivity spectra (5.7-100 µm) were linked to corresponding OCAMS imagery [5] to place the thermal infrared measurements in geological context, by cross-referencing observation times. For every spectrum we derived four diagnostic band parameters: Christiansen Feature (CF), silicate stretching band, silicate bending band and spectral slope, following the methods outlined in [6]. Each site contains thousands of spectral observations (site-averaged for visualization in Figure 1). The corresponding band parameters were compared using three statistical models: Principal Component Analysis (PCA) [5], k-Nearest Neighbors (KNN) [7], and Analysis of Variance (ANOVA) [8]. The three methods compare the mean and variance of each individual observation per site, considering how the in-group variance (i.e. the spread within all observations of a single site) compares to the out-group variance (i.e. the spread from other sites).  Results: Significant differences in emissivity spectra emerged among the four sites. PCA indicated that the first three components explain 85.5% of spectral variance, distinguishing Kingfisher as notably unique, with Sandpiper and Osprey exhibiting the greatest similarity. The KNN analysis corroborated PCA findings, reaching optimal classification accuracy (47%) at k = 21. ANOVA highlighted significant variability among the sites, especially in the spectral slope parameter (F = 762.8), suggesting differences in particle size distribution and space weathering could be driving factors in the detected heterogeneity [9]. Band ratio analyses provided additional insight into site-specific mineralogical distinctions, notably the relationship between silicate features and aqueous alteration indicators [10].  Figure 2: Distributions of Band Parameters by Site Discussion: Variability in spectral parameters aligns with documented particle size frequency distributions and known space weathering spectral types across Bennu’s surface [9]. Nightingale, the mission’s selected sample site, captures representative global characteristics, contrasting with Kingfisher’s distinct compositional and physical attributes, potentially related to differences in Fe/Mg content and degree of aqueous alteration [10].  Conclusion: Integrative use of multiple statistical approaches confirms the compositional and physical diversity of Bennu's surface, as seen through the four prospective sites. These analyses provide a framework for interpreting returned sample data and offer insights into the connections between mineralogy, particle size, and space weathering processes on small airless body surfaces.  References: [1] Lauretta D. S. et al (2021) Sample Return Missions. [2] Hamilton V. et al. (2021) A&A (Vol. 650). [3] Christensen, P. R. et al. (2019) NASA Planetary Data System [4] Christensen P. R. et al. (2018) Space Science Reviews (Vol. 214, Issue 5). [5] Rizk B. et al (2018) Space Science Reviews (Vol. 214, Issue 1). [6] Xie B. et al (2022) Minerals (Vol. 508, Issue 12). [7] Kramer O. (2013) Intelligent Systems Reference Library (13-23). [8] Sawyer S. (2009) Journal of Manual & Manipulative Therapy. [9] Clark B. E. et al (2023) Icarus (Vol. 400). [10] Bates H. et al (2020) MaPS (Vol. 55, Issue 1). 

Abstract:

The ultramafic and silicic rocks on the lunar surface have played an important role in expanding our knowledge regarding its thermal and magmatic evolution. The surface identification and quantification of these rocks on the global scale can significantly improve our understanding of their spatial extents, relationships and formation mechanisms. Christiansen feature positions using Diviner data have aided in global identification and mapping of relatively silica-rich and silica-poor lithologies on the lunar surface. We have used laboratory thermal infrared spectra of silicic and ultramafic rocks to analyze the variation in Christiansen feature in simulated lunar environment. We have characterized the absolute bulk silica content of the rocks and minerals and their Silica, Calcium, Ferrous iron, Magnesium index. We find that they are linearly correlated to the Christiansen feature despite particle size variations. Furthermore, we find that the Christiansen feature shifts toward longer wavelengths with increase in ilmenite content in the ilmenite-basalt mixtures. We have explored the effect of instrument's spectral band position on the accuracy of the parabolic method that is currently used for the estimation of Christiansen feature position from Diviner data. We find that this method performs poorly for the estimation of the Christiansen feature for ultramafic and silicic rocks and minerals/mineral mixtures. We propose using a machine learning algorithm to estimate the Christiansen feature with higher accuracy for all kinds of silicate compositions on the Moon. This method will lead to increased accuracy in absolute quantification of bulk silicate composition of the lunar surface at varying spatial scales.