INTRODUCTION

To meet the needs of space exploration missions, CNES and its industrial partner 3D+ have developed a series of dedicated cameras called CASPEX^[1] (Figure-1). These cameras consist of two parts:

• A detector unit with different numbers of pixels and spectral ranges, as well as filters deposited on it (color, polarization),
• An optical unit mounted on the detector, with different functional points, such as wide-angle cameras for navigation, or tilted proximity cameras to study the composition of the ground under millimeter resolution.

Such cameras are used on Supercam/RMI aboard Perseverance^[2] and will be used on the MBRSC Rashid^[3][5] rovers and on the CNES/DLR Idefix^[4] rover aboard the JAXA MMX mission.

CASPEX Image Processing (CASPIP) is a tool used for the first-level correction processing of the raw images captured by these cameras (Figure-2).

CASPIP SOFTWARE DESCRIPTION

CASPIP is an “easy-to-use” Python library developed at CNES and distributed on https://www.connectbycnes.fr/en/caspip. It works on Linux and Windows. The configuration parameters are grouped in a “camera” object, so that it is possible to define several cameras in one run. The calling interface is step-by-step and as simple as follows:

            processed_image = caspip_function(my_camera, image_to_process, (optional parameters))

In addition to the main functions shown in Figure-2, some tools are added, such as direct and inverse localization functions and distance measurements, as well as a GUI visualization tool.

CASPIP can be used also for enhanced image processing^[5], such as 3D reconstruction.

MAIN FUNCTIONS

Radiometric processing

CASPEX detectors have a PRNU (Photon Response Non Uniformity) of less than 1.35%^[1] and very few defective pixels^[1]. Despite such low levels, CASPIP can use ground data to correct – if needed – PRNU effects, as well as interpolate missing pixels in the image.

The wide-angle cameras induce a strong low-frequency vignetting effect (Figure-6, a→b), which can also be observed on various cameras, such as Supercam/RMI (Figure-3, a→b). The vignetting is corrected by dividing by a flat-field reference image.

Typical color filter arrays provide a sparse representation of the spectral bands using a Bayer pattern, alternating red, blue and green pixels. It is important to perform a ‘demosaicing’ step, to ensure that the final RGB image has the same resolution as the detectors. Several methods can be used for demosaicing, with the most common being based on interpolation. CASPIP implements the Hamilton-Adams^[7] (Figure-6, b→c) and Malvar^[8] algorithms by default, although several other techniques can be implemented instead, such as Bayesian approaches^[6], but with increased computational complexity.

Knowing the Modulation Transfer Function (MTF) of the instrument, preferably by in-situ measurements, it is possible to add a deconvolution step to reverse instrument blurring and sharpen the final images. Depending on the camera and the optical design, this step may be necessary (Supercam/RMI – Figure-3, b→c – in this case, the image is oversampled regarding the optical resolution).

Before deconvolving, it is necessary to reduce the noise, which should be increased by the processing: CASPIP implements a NL-Bayes denoising technique^[6] (Figure-3, d→e).

Finally, a colorimetric correction has to be applied for the image to be expressed in a standard camera-independent color space. This is done by applying a correction matrix calculated following the ISO/CIE 11664-3 standard.

Geometric corrections

Wide-angle cameras cause an important distortion effect that can be corrected by applying a polynomial^[5] to the (x,y) coordinates (Figure-6, d→e). This processing causes a deformation of the image, which is no longer on a regular square grid: we can either crop it or keep the whole image (Figure-4), depending on our needs. The resulting image is nominally regularly sampled with a pixel size in the center of the field still equal to the original resolution.

Observing the microscopic structure of the ground near the rover requires pointing the camera in a tilted direction, which results in a distorted image because the observed plane is not perpendicular to the pointing direction. This is the case for CAM-M on Rashid-1 and for the MMX wheel cameras, which observe the interaction between the wheels and the regolith. CASPIP allows the calculation of "ortho-images" using the value of the tilt angles (Figure-5). If the ground surface is not flat, residual distortion effects are still expected on the result.

FURTHER DEVELOPMENTS

Since proximity cameras have a very small depth of field, an object (rock) or a hole will cause defocus to increase in the field of view. We will then develop a function to make the deconvolution variable across the detector field of view.

Future CASPEX cameras will also include complex filter arrays^[9] with typically 8 or 25 spectral bands, which will require a specific "demosaicing" function. We are working on new solutions that will be implemented in CASPIP. Such cameras and processing will allow both a spectral capability and a good spatial resolution without moving parts such as filter wheels.

Other developments can be considered in the framework of the collaboration with CNES for new space exploration missions.

REFERENCES

[1] Virmontois, C. et al, CASPEX: Camera for space exploration, in preparation (2024)

[2] Wiens, R.C., Maurice, S. et al, Space Sci. Rev. 217, 4 (2021),
https://doi.org/10.1007/s11214-020-00777-5

[3] Els, S.G., Almarzooqi, H. et al, Space Sci. Rev, under submission (2024)

[4] Michel, P. et al, Earth, Planets and Space 74(1), 2 (2022),
https://doi.org/10.1186/s40623-021-01464-7

[5] Théret, N. et al, Space Sci. Rev, under submission (2024)

[6] M. Lebrun et al, IPOL, vol.3, pp.1-42 (2013), http://dx.doi.org/10.1137/120874989

[7] Hamilton J.F.Jr., Adams J.E.Jr., Patent US5629734A (1996),
https://patents.google.com/patent/US5629734A/en

[8] Malvar, H.S. et al, International Conference on Acoustics, Speech, and Signal Processing,
pp.iii-485 (2004), https://doi.org/10.1109/ICASSP.2004.1326587

[9] Cucchetti, E. et al, Color and Imaging Conf.,  pp205-212 (2022),  
https://doi.org/10.2352/CIC.2022.30.1.36

How to cite: Théret, N., Douaglin, Q., Cucchetti, E., Robert, E., Lucas, S., Lalucaa, V., and Virmontois, C.: CASPEX Image Processing: a tool for space exploration, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-15, https://doi.org/10.5194/epsc2024-15, 2024.

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Introduction: The Context Camera (CTX) has so far delivered more than 145,000 images [1]. The images are one of the most popular datasets for planetary geologists, providing extensive coverage, excellent radiometric resolution, and a unique resource for interpreting surface features. The Integrated Software for Imagers and Spectrometers (ISIS) is a software to support the ingestion, processing and analysis of planetary image data [2] and is the standard processing framework for CTX. Since the beginning of its mission, calibrated images from CTX have shown a subtle darkening effect from the centre of the image towards the edges. Due to its typical shape when plotted as a profile, this effect has been called the "frown" effect (see Figure 1).

Figure 1: Subset of CTX image G09_­021566­_1800 after nominal ISIS calibration together with a plot of the reflectance values averaged over all lines [3].

Some authors assume a varying darkening effect over the mission time, and workarounds to correct for this have been described [4,5]. Here we present an up­dated flat-field calibration for ISIS that removes the edge darkening effect. We ensure that it does not introduce any artefacts and perform quantitative validation of calibrated images to show the validity of the improvements.

Methods: The overall shape of the CTX flat-field is a curve, where the difference between the centre and the edges of the detector represents a quantifiable amount of darkening caused by lens vignetting. To quantify the amount of this edge darkening correction by a flat-field, we use the concept of the frown factor. Similar to the quantification of the band depth feature in spectral analysis, the amount of darkening correction by a flat-field can be expressed as the ratio of the mean values of the central area of the flat field to the mean values of its edges (see Figure 2).

Figure 2: Elements for the composition of the frown factor of a flat-field [3].

Before building a flat-field from a pool of input images, we correct the input data from bias and dark-current effects without any initial flat-field correction. As the ISIS ctxcal command combines these two corrections, we turn off the flat-field correction in ctxcal by providing a custom flat-field file where all values are set to one. We use the resulting pre-processed bias/dark-current corrected files for calculating the new flat-field.

Figure 3: Top: Scatter plot of the frown factor over time. Bottom: mean monthly frown factor over time [3].

Results: Figure 3 shows the temporal evolution and distribution of the frown factor, i.e. the amount of darkening towards the detector edges. We exclude images exhibiting overexposure or the ones with negative reflectance values after calibration. The frown factor remains relatively consistent around its mean of 1.55 until the end of 2018, followed by a subsequent rise until early 2020. Throughout this interval, the average frown factor for all images rises to approximately 1.6, and throughout 2020, it reverts to the value observed before the mentioned change.

Investigating the distribution of the frown factor excluding images from the problematic year 2019, we observe a skewed data distribution (Figure 4 right). As the frown factor outside the irregular time interval from 2019 until early 2020 appears very stable, we can safely assume that the edge-darkening effect is stable over time. The deviation from its mean during the period in question is not representative, as the central limit theorem is not fulfilled, which we observe in its non-symmetrical distribution.

Figure 4: Kernel density plot of the frown factor of all images except the ones from the year 2019 (left) compared with the  ones from year 2019 (right) [3].

Evaluation: One of the main advantages of the improved calibration is better in-image stability for image mosaicking, which leads to homogeneous mosaics. An example of this is provided in Figure 5. The seams between adjacent images are strongly visible in the "before" mosaic (case a), processed with the nominal flat-field in ISIS. Using our new flat-field calibration file, most seams are no longer visible (case b).

Figure 5: Example CTX mosaic of the Oxia Planum region. Images were chosen from Martian Year 33. a) calibrated with the nominal ISIS internal flat-field calibration – b) calibrated with the new global flat-field calibration [3].

For a quantitative evaluation, we randomly chose 10.000 images over the full timespan and calibrated them with  our new flat-field file when using ctxcal. The arithmetic mean of the frown factor of these images is then 1.00, proving a very good result and confirming the frown factor as a good quantification. We randomly reduced the subset to 1.000 images and performed a systematic visual inspection for qualitative evaluation. We could not find any signs of a remaining edge-darkening effect during the visual investigation. The residual edge-darkening effect is 1.079, calculated as the arithmetic mean over the individual factors of all images from the validation dataset calibrated with the previous flat-field file. This means, a surface recflectance measurement taken at the edge of a CTX image appears 8% darker than in the center, when calibrated with the previously available flat-field file.

The new flat-field calibration file is available from this data repository: http://dx.doi.org/10.17169/refubium-41645. It has also been provided to the ISIS development team in order to publish it in their default data directory, beginning with ISIS version 9.0.

Acknowledgements: This work is supported by the German Space Agency (DLR Bonn), grant 50 OO 2204, on behalf of the German Federal Ministry for Economic Affairs and Energy.

References: [1] M. C. Malin et al., JGR Planets (2007). [2] Jason Laura et al., 2023, DOI: 10.5281/zenodo.2563341. [3] Walter, et al., ESS 11 (2024), DOI: 10.1029/2023EA003491. [4] J. L. Dickson et al., LPSC 49 (2018), #2480. [5] Stuart J. Robbins et al., ESS 10 (2020).

How to cite: Walter, S., Aye, M., Jaumann, R., and Postberg, F.: Mars Reconnaissance Orbiter Context Camera updated in-flight calibration, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-77, https://doi.org/10.5194/epsc2024-77, 2024.

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We present recent advances in our Asteroid Imaging Simulator (AIS) toolset [1]. The purpose of the tool is to support the design and operating planning of imaging instruments in space missions. In particular, we aim to support the hyperspectral camera units from VTT Finland in ESA Hera mission (ASPECT camera, Milani CubeSat) and in Comet Interceptor mission (part of MIRMIS instrument).

The tool consists of two main parts. The part generating simulated imagery is based on Blender ray-tracing software [2], and the part converting the images into image data with physical units and simulated instrument noises is written in Python. The Blender part utilizes the internal Python interpreter on Blender and all the tools from the Blender GUI. Our Python module and Blender file enable using shape models as targets, e.g., we have Benny, Ryugu, Itokawa, Didymos-Dimorphos, and 67P/Churyumov–Gerasimenko as possible targets. Additionally, we concentrate on providing typical photometric models for the target surfaces including Lommel-Seeliger, ROLO, McEwen, and Lambert photometric functions that are implemented using Blenders’ shaders. The coordinate and attitude information from SPICE kernels is integrated into our Blender package, as well as scripts for creating circular or elliptical orbits around the target or fly-by scenarios.

The most recent activity is in the coma model to be used with the Comet Interceptor mission. As we aim to simulate and help camera operations, the actual structure of the coma or jet structure etc. is not of main interest to us, whereas the scattering properties of gas and dust in the coma, given some simple model of their density around the nucleus, is. We will present the first evaluation of the gas and dust coma effects on the scattering in the simulated images of a comet nucleus and surrounding environment. We will concentrate on the target scenarios of Comet Interceptor.

Our post-processing Python package handles the conversion of unitless images from Blender into simulated data stream from an imaging unit with physical units and noises. We will first convert the image color (grayscale) space into spectral radiance in W/m²/nm/sr with the knowledge of target albedo, reflectance spectra, and its distance to Sun. Second, knowing the camera and detector specifications, we can convert all the way into charge counts on the CCD pixel with given integration time. Finally, the detector noise profile allows to compute the expected signal-to-noise ratio of the observations, and to simulate the output data stream with noises. This helps in designing integration times and verifying, e.g., reliable detection of features in the retrieved spectrum.

Figure 1: Example of generated images using the shape model of Bennu in two camera positions.

[1] Git project for the Asteroid Imaging Simulator, https://bitbucket.org/planetarysystemresearch/asteroid-image-simulator/
[2] Blender software, https://www.blender.org

How to cite: Penttilä, A., Kolehmainen, M., Halla-aho, N., Jääskeläinen, J., Palos, M. F., Näsilä, A., Cole, R., and Kohout, T.: Imaging instrument simulations for ESA Hera and Comet Interceptor missions, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-345, https://doi.org/10.5194/epsc2024-345, 2024.

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Introduction

Major rock-forming minerals such as pyroxenes are very common in the solar system and show characteristic absorption bands due to Fe²⁺ in the VIS and NIR [e.g., 1, 2]. The Fe-free endmember enstatite is also probably a common mineral on planetary surfaces such as E-type asteroids and Mercury [3] and a major constituent of meteorites such as aubrites [4] and enstatite chondrites [5]. Reflectance spectra of these meteorites as well as of enstatite-rich or generally Fe-poor asteroids like E-type asteroids and Mercury are often featureless in the VIS and NIR, lacking the absorption features associated with iron incorporated into the crystal structure of silicates, while Fe-bearing orthopyroxenes show diagnostic absorption bands at ~1 µm and ~2 µm [1,2].

For a better understanding of these Fe-poor bodies, the availability of laboratory spectra of Fe-free silicates as analog materials is crucial, but terrestrial samples of enstatite usually contain several mol% of FeO, with pure enstatite being extremely rare. In order to make larger amounts of pure enstatite readily available, we have synthesized nearly FeO-free enstatite [6].

We present 0.3-16 µm reflectance spectra of synthetic enstatite (Mg₂Si₂O₆), synthetic oldhamite (CaS), and of their mixtures [7] for comparison with spectra of E[II]-type asteroids, investigate the spectral behavior of the mixtures with respect to their oldhamite content, and discuss the implications for surface composition analysis of Mercury.

Selection of Samples

All reflectance spectra were collected using a Bruker Vertex 80v FTIR spectrometer at the Planetary Spectroscopy Laboratory of the Institute of Planetary Research at DLR, Berlin [8]. The synthesis of the enstatite sample with the composition En_99.6Fs_0.0Wo_0.4 has been described in detail by [6]. The oldhamite sample was purchased from abcr GmbH (99.95 %, metal basis, CAS #20548-54-3). The average grain size of the enstatite and oldhamite samples are 26 µm and 12 µm respectively [6,7]. Mixtures containing 1, 3, 5, 10, 20, 30, 40, 50, 60, 70, 80, and 90 vol% oldhamite were prepared.

Results

Figure 1 shows the reflectance spectra from 0.3 to 1 µm. The enstatite spectrum shows several weaker absorption bands between 0.4 µm and 1 µm which are due to Fe³⁺ and possibly Ti and shows a steep red slope in the UV. The spectral slopes in the VIS and NIR are nearly neutral with only a slightly reddish slope in the VIS. The spectrum of synthetic oldhamite shows an absorption band at 0.41 μm with a relative depth of 11.4 %. This band is visible in the spectra of all mixtures, even in the spectrum of the mixture containing only 1 vol% oldhamite. In the MIR, the spectra with ≤10 vol% oldhamite are very similar to the spectrum of the pure enstatite and are generally dominated by the Christiansen feature (CF) and the Reststrahlen bands (RB). The pure oldhamite is significantly brighter than the enstatite spectrum in the MIR and shows several broad and overlapping absorption features (figure 2). Changes in the band depth and reflectance, both in the VIS and MIR, do not occur as a single trend, but follow two distinct trends. One for mixtures with ≤10 vol% of oldhamite, where changes rapidly occur, and another trend for mixtures with ≥20 vol% of oldhamite, where changes occur more slowly.

Figure 1. Reflectance spectra of synthetic enstatite, oldhamite, and their mixtures from 0.3 to 1 µm. Compositions are given in vol%.

Figure 2. MIR reflectance spectra from 7 to 16 µm of synthetic enstatite, oldhamite, and their mixtures. For legend see figure 1.

Discussion

Based on XRS data, sulfur contents up to 4 wt% have been reported in surface materials of Mercury [9], and sulfide decomposition has been linked to the formation of hollows on Mercury [10], geological features unique to the surface of Mercury. Oldhamite was proposed as the major lithophile sulfide on the surface of Mercury [11]. Therefore, understanding the spectral behavior of oldhamite in mixtures with FeO-poor silicates, such as enstatite, is crucial for the interpretation of spectral data from Mercury. Figure 3 shows a comparison between a Mercury spectrum [12,13], the aubrite Peña Blanca Spring (PBS) [6], and selected laboratory spectra from this study. The positions of the CF and RB in these spectra are in good agreement. The RBs are less pronounced in the enstatite and PBS spectra than in the Mercury spectrum. The oldhamite spectrum has a local maximum at ~9.5 µm, which could contribute to the higher reflectance in this wavelength range compared to the enstatite and PBS.

The influence of space weathering on FeO-poor surfaces such as Mercury is still poorly understood. Factors such as thermal degradation or the influence of grain size changes have been discussed. However, many important influencing factors are still unknown [7]. Spectral investigations of the samples at Mercury-relevant surface temperatures can improve our understanding of these processes. At the same time, such studies would improve the comparability of Mercury spectra with those of the analogous materials measured here at room temperature. 

Figure 3. Comparison between a Mercury spectrum [12,13], Peña Blanca Spring spectrum [6], and selected laboratory spectra from this study. The Mercury spectrum was obtained by [12] and converted to reflectance using Kirchhoff’s law and baseline corrected by [13]. Compositions are given in vol%.

References

[1] Burns (1993) Mineralogical Applications of Crystal Field Theory, 2^nd ed. [2] Klima et al. (2007) Met. Planet. Sci., 42, 235-253. [3] Izenberg et al. (2014) Icarus, 228, 364-374. [4] Keil (2010) Chem. Erde, 70, 295-317. [5] Mason (1968) Lithos, 1, 1-11. [6] Markus et al. (2018) Planet. Space Sci., 159, 43-55. [7] Markus et al. (2024) Planet. Space Sci., 244, 105887. [8] Maturilli and Helbert (2019) LPSC, 1846. [9] Nittler et al. (2011) Science, 333, 1847-1850. [10] Blewett et al. (2013) JGR Planets, 118, 1013-1032. [11] Vaughan et al. (2013) LPSC, 1719. [12] Sprague et al. (2000) Icarus, 147, 421-432. [13] Morlok et al. (2020) Met. Planet. Sci., 55, 2080-2096.

How to cite: Markus, K., Arnold, G., Moroz, L., Henckel, D., and Hiesinger, H.: Laboratory reflectance spectra of enstatite and oldhamite mixtures for comparison with E-type asteroids and implications for Mercury’s surface composition analysis, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-446, https://doi.org/10.5194/epsc2024-446, 2024.

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Introduction:

The JAXA/Martian Moon eXploration (MMX) mission will be launched to the Martian system in 2026 with the objective of deciphering the origins of Phobos and Deimos [1]. One of the instruments onboard the MMX spacecraft will be MIRS, an imaging-infrared spectrometer covering a wavelength range from 0.9 µm to 3.6 µm [2]. In this work, we will present simulations of MIRS imaging of Phobos realized to prepare the future science exploitation of MIRS data and assess instrument performances. These simulations will also be useful during the operations of MIRS to anticipate the operations such as the Deimos flybys and Phobos landing.

Figure 1:  Example of surface reflectance at 1 µm (left) and temperature at 3 µm (right) landscapes obtained with OASIS simulation.

Method:

The generation of a spectral MIRS image is obtained with the use of a suit of three simulators. The observation mission scenarios are initially derived through AURORA [3], which provides all necessary orbital and instrument information, including the spacecraft position, pointing, etc. The second simulator, OASIS [4], is dedicated to the generation of all the geometric information (incidence, emission, phase, etc.) for each pixel/facet of the Phobos surface. AURORA provides the observation geometries fed into OASIS. The tool considers topographic variation on the surface through the use of the Phobos shape model. From the obtained illumination angles, a Hapke model is used to compute the bidirectional reflectance. The thermal component of the spectra is accounted for, through the use of a Standard Thermal Model. The current model does not account for multiple scattering and Mars reflected light on Phobos and Deimos.

To avoid extremely long computational time, the geometric calculations are only performed at a single wavelength. The spatial and spectral terms of the solar and thermal irradiance are decoupled to allow extrapolation of a spectrum from the simulated landscape.

After generating the reflectance and temperature maps (Fig. 1), the MIRAGES simulator [5] is run to model the MIRS instrument response, including light propagation through the mirrors, grating, and other optical elements, as well as accounting for the radiometric efficiency of these components, and introducing a geometric distortion. This allows to obtain 2D MIRS raw images. In the final stage, the images are processed further using a portion of the MIRS pipeline to obtain corrected and calibrated images. This includes distortion correction, ADU to I/F conversion, and spectral registration procedures. A thermal correction is subsequently applied to remove the contribution of the thermal tail.

Results:

The MIRS images were simulated for two scheduled observations of Phobos, designated as QSO-H (quasi-stationary orbit at high altitude) and QSO-M (quasi-stationary orbit at medium altitude). A typical rendered image for a QSO-H orbit is presented in Fig. 2.

Figure 2: Example of a typical MIRS image for QSO-H orbit, obtained after the three simulators.

In addition to the interest of having the capacity to simulate relatively rapidly MIRS images, we also investigated the detectability of several components of interest for the Phobos’ surface. In particular, we assessed the detectability of small patches of hydrated minerals and organics at the surface of Phobos by MIRS. For this purpose, we used as input, the experimental spectra obtained in our previous works for detectability studies in a Phobos regolith simulant [6,7]. We defined patches of different sizes from a diameter of 3.5 km to 12.9 m at the surface of Phobos. We found that the homogeneous patches are visible in the QSO-H orbit for a diameter of approximately 40 m but not visible when the diameter is reduced to ~13 m. This is in agreement with the expected spatial resolution in the QSO-H orbit which was estimated to be between 31 and 66 m/px. An example of a MIRS observation sequence is presented in Fig. 3. Whereas MIRS images 1 and 2 of the figure correspond to a “classic” area on Phobos, image 3 clearly shows a bright line at the center of the images. This bright line corresponds to MIRS scanning over a small patch of phyllosilicate in our simulated Phobos surface model.

From these 2D images, it is possible to extract the 1D spectrum of both organic and classic Phobos regions. Typical obtained spectra are presented in Fig. 3 (bottom-left corner panel). The contrast between the two regions is evident when looking at the 2.7 µm region, where an absorption band is visible in the case of the phyllosilicates-rich patch compared to the classic Phobos surface.

Figure 3:  Example of 5 images of a MIRS observation sequence on a landscape obtained by OASIS. The passage on the hydrated mineral patch can be visually seen as a bright line through the spectral direction in the center of images 3 and 4. Bottom left corner: Example of two 1D spectra extracted for a MIRS image (panel 3), before thermal tail removal. The black solid line corresponds to the phyllosilicate patch MIRS rendered spectrum, and the red solid line corresponds to the ‘classic’ Phobos surface MIRS spectrum. The 2.7 µm band due to OH in hydrated minerals is visible in the black spectrum.

Conclusion:

Our simulations allow to explore the detectability of key components for Phobos and Deimos, with a particular focus on organics and hydrated minerals, for a typical QSO-H orbit. In further investigation, we plan to assess the detectability of inhomogeneous and/or mixed patches as well as addressing other MMX orbits and Deimos flybys. The simulations of Phobos and Deimos observations will be pivotable to prepare the MMX mission.

Acknowledgments: The authors acknowledge the Centre National d’Etudes Spatiales (CNES) for the continuous support.

References: [1] Kuramoto et al. (2021), EPS, 74, 12 [2] Barucci et al. (2021), EPS, 73, 211 [3] Sawyer et al. (2023), Acta Astronautica, 210 [4] Jorda et al. (2010), SPIE Symposium [5] Théret et al. (2023), LPSC [6] Wargnier et al. (2023), MNRAS, 524, 3 [7] Wargnier et al., submitted to Icarus (10.48550/arXiv.2405.02999)

How to cite: Wargnier, A., Gautier, T., Jorda, L., Théret, N., Doressoundiram, A., Mathé, C., Fornasier, S., and Barucci, A.: Simulations of MMX/MIRS Phobos observations, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-462, https://doi.org/10.5194/epsc2024-462, 2024.

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Introduction:

The observed presence and orientations of fractures on boulders on the asteroids  (101955) Bennu and (65803) Didymos-Dimorphos have been instrumental in establishing the role of thermal fatigue and fragmentation on asteroids (Molaro et al., 2020, Lucchetti et al. 2024). With cratering processes, thermal fatigue is hypothesized to be one of the primary mechanisms forming regolith on small asteroids (Delbo et al., 2014).  However, lighting and viewing conditions can introduce ambiguity in the orientation and number of surface lineaments  that are identified on an asteroid’s surface (Buczkowski et al., 2008). Similarly, lighting conditions can affect the identification and orientation of fractures observed on boulders. Therefore, any unidentified bias in determining the orientation of these boulder fractures could influence any conclusions on the importance of thermal fatigue relative to cratering.

To understand what imaging bias might exist, and to determine a way to correct for it, we explore systematically how lighting influences measurement of fracture orientations on asteroid boulders. We render several local digital terrain models (DTMs) of boulders on Bennu generated using data collected by the OSIRIS-REx Laser Altimeter (OLA; Daly et al., 2020, 2017). The modeled boulders were chosen because they possess numerous linear structures on their surfaces that are well captured by the high-resolution, 5 cm per pixel ground sample distance (GSD) of the OLA-derived DTMs. The boulders considered in this study range in size from 6–33 m in diameter.

Figure 1. Setup used for rendering surface boulders observed by OLA on Bennu. Camera is looking straight down on surface with an elevation on 90°

Methodology:

In this analysis, each local boulder DTM had its slope de-trended, such that the boulder was sitting on a flat-horizontal plane.  Images of the boulder were rendered using a ray tracing algorithm and a modified Lommel-Seeliger photometric function identical to what was done in Daly et al., (2022). The camera was always placed at an elevation angle of 90° (0° emission) and a range of 1 km from the surface, looking directly down at the boulder (Figure 1). The modeled camera had 1024x1024 pixels with a focal length of 500 mm and an instantaneous field of view (IFoV) of 4x105 radians, resulting in images with a ground sample distance of 4 cm/px. These camera properties were picked so that the resulting image pixel scales were similar to the GSD of the DTMs. The boulder DTMs were rendered using a range of solar azimuths (α of 0° to 360°) and elevations (β -30° to 30° via 90°) (Figure 2). Images were rendered every 30° in α and 20° in β.

Figure 2. Boulder images rendered for differing sun azimuths α and elevations β. The image with the yellow outline indicates the observing conditions that existed during the DART impact (Daly et al., 2023).

Members of our team were then tasked with mapping lineaments on a subset of the synthetic images. Each investigator was only given images for one lighting condition when they undertook their lineament assessment, in order to minimize any bias that might have arisen by having more than one viewing orientation. They were also given a downsampled and smoothed version of the boulder DTM so that they could map lineaments on the rendered images in the Small Body Mapping Tool (SBMT, Ernst et al. 2018) if they so desired. Once all the rendered images were mapped and the result turned in, each investigator was given a full resolution boulder DTM with all of its renderings, so that a complete set of lineaments could be identified. These were then compared to the lineaments obtained from the individual rendered images. The results for any biases were then assessed.

Preliminary results:

The mapping is ongoing, and a full report of our findings will be presented at EPSC 2024. However, some preliminary findings can be mentioned. As is already well-known, the morphology of surface features, including boulder fractures, are not easily recognized when the sun elevation is high (immediately overhead): features get washed out. Images taken with low sun β (<20°) are also problematic because shadows hide any significant morphology. The best viewing geometry for finding boulder fractures seems to be between 20°< β <60° solar elevation. Using two orthogonal solar azimuths further reduce bias.

For lighting at the DART impact site (where the Sun was at about β = 30° and α= 10°), many of the fractures present on boulders can probably be identified. Cracks that are orientated in N-S, NE-SW and NW-SE are easily identifiable. Some E-W fractures could be missed.

Conclusion:

Lighting needs to be considered when evaluating orientation of boulder fractures. Biases related to lighting geometries may have implications for past fracture studies on Bennu and elsewhere. The evidence used to establish the importance of thermal fatigue operating on asteroids may need to be re-evaluated.

Acknowledgemnet: O.S.B and R.-L.B. are funded by NASA New Frontiers Data Analysis Program grant number 80NSSC22K1035.

References:

Buczkowski, D.L., Barnouin, O.S., Prockter, L.M., 2008. 433 Eros lineaments: Global mapping and analysis. Icarus 193, 39–52.

Daly, M.G., Barnouin, O.S., Dickinson, et al., 2017. The OSIRIS-REx Laser Altimeter (OLA) Investigation and Instrument. Space Science Reviews 212, 899–924.

Daly, M.G., Barnouin, O.S., Seabrook, J.A. et al.., 2020. Hemispherical differences in the shape and topography of asteroid (101955) Bennu. Science Advances 6. https://doi.org/10.1126/sciadv.abd3649

Daly, R.T., Ernst, C.M., Barnouin, O.S., et al., 2023. Successful Kinetic Impact into an Asteroid for Planetary Defense. Nature 1–3.

Daly, R.T., Ernst, C.M., Barnouin, O.S., et al., 2022. Shape Modeling of Dimorphos for the Double Asteroid Redirection Test (DART). Planet. Sci. J. 3, 207.

Delbo, M., Libourel, G., Wilkerson, J., et al., 2014. Thermal fatigue as the origin of regolith on small asteroids. Nature 508, 233–236.

Ernst, C.M., Barnouin, O.S., Daly, R.T., et al. 2018. The Small Body Mapping Tool (SBMT) for Accessing, Visualizing, and Analyzing Spacecraft Data in Three Dimensions,  LPSC 48, abstract 1043.

Molaro, J.L., Walsh, K.J., Jawin, et al., 2020. In situ evidence of thermally induced rock breakdown widespread on Bennu’s surface. Nature Communications 1–11.

Lucchetti, A., Cambioni, S., Nakano, R. et al., 2024. Fast boulder fracturing by thermal fatigue detected on stony asteroids. Nature Communications, accepted.

How to cite: Barnouin, O., Ballouz, R., Lucchetti, A., Ernst, C., Parro, L., Kinczyk, M., Daly, T., Pajola, M., and Tusberti, F.: Fractures on Asteroid Boulders: Understanding Orientation Biases Caused by Lighting , Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-555, https://doi.org/10.5194/epsc2024-555, 2024.

EPSC2024-555

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Interpreting the surface composition of the Moon and other airless bodies relies heavily on spectroscopic techniques spanning visible and near-infrared wavelengths. This study employed an integrated spectroscopy approach to measure and characterize a suite of lunar rock samples, a lunar meteorite, and lunar minerals. The primary objective was to demonstrate the methodology for building a robust spectral library that can support the interpretation of data from future lunar missions focused on understanding surface compositions and resource potential.

The analog samples characterized included 15 lunar mineral endmembers (pigeonite, augite, ilmenite, orthopyroxenes, olivine, anorthite, and bytownite), an Apollo 16 regolith sample (62231.44), and the Tisserlitine 001 lunar meteorite. These materials were prepared as fine powders sieved to grain sizes ranging from 25 to 125 micrometers. Spectroscopic measurements were performed under vacuum conditions using the state-of-the-art Bruker Vertex 80V FTIR instruments at the Planetary Spectroscopy Laboratory. Bidirectional reflectance spectroscopy was employed to acquire spectra at illumination angles of 30° and 45° relative to the sample surface normal, utilizing attached visible and near-infrared detectors. Hemispherical reflectance measurements were conducted to collect visible and near-infrared spectra separately, using integrating spheres.  The calibrated hemispherical reflectance spectrum for the Apollo 16 sample exhibits a clear hydroxyl group feature near 3 micrometers, consistent with previous observations. Despite originating from different locations, the Apollo 16 lunar sample and the Tisserlitine 001 lunar meteorite displayed striking similarities in their visible and near-infrared bidirectional reflectance spectra, exhibiting absorption features characteristic of lunar highland compositions, including broad 1 and 2 micrometer bands indicating the presence of pyroxenes. However, the Tisserlitine 001 sample had slightly more pronounced pyroxene bands, although the overall spectral shape and absorption features were comparable between the two samples.

Further spectral analysis revealed notable observations. Ilmenite exhibited the highest overall reflectance, followed by orthopyroxene and pigeonite, while anorthite and bytownite displayed relatively low reflectance across most wavelengths. Olivine exhibited a distinct 10 micrometer absorption feature. Interestingly, the Tisserlitine 001 lunar meteorite displayed a unique spectral signature distinct from the other minerals studied. The study's findings contribute to developing a robust spectral library to aid in identifying lunar mineralogy and chemistry from orbital or landed measurements, enabling assessments of resource potential and improving understanding of lunar formation processes.

How to cite: Tripathi, P., Maturilli, A., and Alemanno, G.: Characterizing Lunar Materials: Spectral Analysis of Apollo 16 and Lunar Meteorite, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-668, https://doi.org/10.5194/epsc2024-668, 2024.

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How to cite: Pentikäinen, H., MacLennan, E., Uvarova, E., Muinonen, K., Penttilä, A., Wilawer, E., Oszkiewicz, D., Cellino, A., Tanga, P., Wang, X., and Virkki, A.: Asteroid photometric and spectroscopic studies with Gaia DR3 data, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-894, https://doi.org/10.5194/epsc2024-894, 2024.

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Introduction

The quantitative estimation of volumetric abundance of powder mixture is the basis of quantitative remote sensing analysis. Here we propose to analyze a unique laboratory measurements set, with precise composition, grain size and volumetric abundance.

We first propose a method to estimate the optical constant of materials, knowing the pure endmember spectra and their grain size. Then, we propose a method to transfer the measurement uncertainties to the volumetric abundance, based on the Bayesian approach and the full Hapke radiative transfer model. Using this approach, we are able to estimate grain size, volumetric abundance, and surface roughness.

The results show that this approach is able to well estimate the correct volumetric abundance with an uncertainty of 23% and grain size with a ratio uncertainty of 3.0, i.e. uncertainties in log10(grain size)=0.48. The numerical cost of the MCMC is quite large (a few minutes per spectra) but still reasonable to treat a hyperspectral image with the gain of robust handling of non-linearities and propagating the uncertainties.

Data

We used the laboratory data set containing 63 spectra, including 14 pure mineral powders and 49 binary mixtures of mineral powders (Koirala et al., 2021). These mixtures were created using 5 selected pure mineral powders: Aluminum oxide (Al₂O₃), Calcium oxide (CaO), Iron oxide (Fe₂O3₃), Silicon dioxide (SiO₂), and Titanium dioxide (TiO₂). All mineral powders have a white color in the visible (except for Iron oxide which is red). Furthermore, they vary in densities and grain sizes. Although there are 10 possible combinations of these powders, due to experimental constraints the dataset contains exclusively seven binary mixture combinations of minerals namely: Al₂O₃-SiO₂ (Al-Si), CaO-SiO₂ (Ca-Si), CaO-TiO₂ (Ca-Ti), Fe₂O₃-Al₂O₃ (Fe-Al), Fe₂O₃-CaO (Fe-Ca), Fe₂O₃ SiO2 (Fe-Si), and SiO₂-TiO₂ (Si-Ti). The provided dataset is particularly difficult to handle, without renormalization since several measurements of the same pure endmember are reproduced with a significant absolute level variability (see Figure 1).

Figure 1 : Spectra of binary granular material mixtures. (left) example of binary mixture Si-Ti (right) Several observation of the same endmembers.

Method

We used the semi-analytical reflectance model from (Hapke, 2012), which is a good compromise between physical realism and efficient computation time. In Andrieu et al., 2022, we demonstrated that the usual gradient-descent method is not helpful because the non-linearities are so strong that the results mainly depend on the initialization. So the analysis is done by a Monte Carlo Bayesian approach to propagate the uncertainties from the measurement in reflectance to the parameters (Cruz Mermy et al., 2023). This operation is sometimes called inversion or assimilation. 

Results

Figure 2 presents a typical result. This particular one is for a CaSi mixture with X_Ca=57.0%. The best fit is extremely close to the real observation. In addition, due to the uncertainties on the reflectance spectra, a range of solutions is acceptable. Figure 3 shows the corresponding posterior PDF for the four parameters that are unknowns: roughness θ₀, volumetric proportion of X_Ca, grains size log10(D_Ca) and log10(D_Si). All marginal PDFs are well constrained with a bell shape, except the log10(D_Si) which is less constrained. The bivariate PDF indicates the relationship between the parameters. For instance, roughness seems highly correlated with the X_Ca.

Figure 3 shows the estimation of the abundance for the Al-Si mixture case. It shows that the trend is well reproduced in all cases. The global RMS is 23.0% for proportions X and 48.9% for log10(D). When the grain size is known, the RMS for X reduces to 21.1%.

Figure 2 : Posterior PDF of the same case as Fig. 2 for the four unknown parameters: roughness θ₀, volumetric proportion of X_Ca, grains size log10(D_Ca) and log10(D_Si). The median values are indicated by a dashed line.

Figure 3 : True versus estimated volumetric abundance for all binary mixtures, estimating the four following parameters: roughness θ₀, volumetric proportion X, grains sizes. The median and the 68.3% quantiles are plotted as the error bar.

Conclusion and perspective

We proposed a methodology to characterize powder samples from the shortwave infrared reflectance dataset (Schmidt et al., 2024). The methodology does not require the optical constant but only pure endmember spectra. In real remote sensing data, this is often not present in the scene. It may be possible to estimate endmembers by utilizing blind spectral unmixing methods such as the one presented in Ceamanos et al., 2011. Of course, if the optical constants are available, this first step can be bypassed. 

Interestingly, the knowledge of the grain size does not help much to constraint the abundance due to the absolute level uncertainties due to absolute level uncertainties (see Fig. 1 right). This effect is difficult to handle in the lab, due to variability in roughness, compaction, illumination condition, calibration and should be even more difficult for real data.

Reference

Andrieu, F. Schmidt, G. Cruz-Mermy, I. Belgacem, and T. Cornet, Benchmarking Bayesian methods for spectroscopy,” in European Planetary Science Congress, Sep. 2022, pp. EPSC2022–502.

Bioucas-Dias, J.; Plaza, A.; Dobigeon, N.; Parente, M.; Du, Q.; Gader, P. & Chanussot, J. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 2012, 5, 354-379, 

Ceamanos, X.; Doute, S.; Luo, B.; Schmidt, F.; Jouannic, G. & Chanussot, J. Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study Geoscience and Remote Sensing, IEEE Transactions on, 2011, 49, 4341-4358, http://dx.doi.org/10.1109/TGRS.2011.2140377

Cruz Mermy, G.; Schmidt, F.; Andrieu, F.; Cornet, T.; Belgacem, I. & Altobelli, N. Selection of chemical species for Europa's surface using Galileo/NIMS Icarus, Elsevier BV, 2023, 115379, http://dx.doi.org/10.1016/j.icarus.2022.115379

Hapke, Theory of Reflectance and Emittance Spectroscopy, 2nd ed, Cambridge University Press, 2012.

Koirala, B.; Zahiri, Z.; Lamberti, A. & Scheunders, P. Robust Supervised Method for Nonlinear Spectral Unmixing Accounting for Endmember Variability IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers (IEEE), 2021, 59, 7434-7448, http://dx.doi.org/10.1109/tgrs.2020.3031012

Schmidt, F.; Koirala, B., Andrieu, F., Determination of volumetric abundance of intimate mixture using Bayesian MCMC, 2024, under review in IEEE Sensor

How to cite: Schmidt, F., Koirala, B., and Andrieu, F.: Validation of abundance determination of granular mixture using radiative transfer and Bayesian MCMC, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-902, https://doi.org/10.5194/epsc2024-902, 2024.

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I. Introduction
The JUpiter ICy Moons Explorer (JUICE) is a mission funded and led by the
European Space Agency (ESA) in the context of its Cosmic Vision program.
The Japanese space agency (JAXA) and the United States' NASA contributed to
this mission. The spacecraft carries 11 instruments designed to observe and
monitor the Jupiter system, with a focus on the Galilean moons Callisto,
Europe and Ganymede, in the intent of furthering our present knowledge of
the Solar System and our understanding of planetary formations, two key
themes of the Cosmic Vision programi [1].
We will present the program (GeoMAJIS) we designed, developed and deployed
to help with the interpretation of JUICE's visible and infrared spectrometer
(MAJIS) observations.

II. The MAJIS spectrometer
MAJIS is a push-broom imaging spectrometer designed to observe the atmosphere
of Jupiter and characterize the surface of its moons from the visible to the
thermal infrared domains (.4 - 5.6 µm) [1]. This instrument has two channels
sharing the same field-of-view (FOV), each equipped with a Teledyne H1RG
1024x1024 detector, with one optimised for the 0.4-2.35 µm domain (VISNIR)
and the other for the 2.25-5.6 µm domain (IR). The detectors are composed
of squared pixels with an instantaneous FOV of 75 µrad.  In the baseline mode, a x2
binning is applied on each detector to increase the signal, such that the FOV
is provided by a 1x400 binned pixels segment, i.e. a 8.5°x10-3*3.43° window.
When orbiting Jupiter from a distance of 10^6 km (or Ganymede at 500 km),
MAJIS' spatial resolution will be of 150 km/pxl (respectively 75 m/pxl).
The instrument also possesses a mirror which allows to shift its FOV in
the along-track axis by up to 2° in either direction.
As MAJIS will perform in a complex observational context, in which
occultations are common, it is necessary to complement the calibrated
observations with ancillary data to ease their interpretation.

III. The GeoMAJIS code
This program relies on the CSPICE library [3] to
perform the necessary computations to detail the observational context,
based on the spacecraft' predicted (or recomputed) trajectory and
attitude, as well as celestial bodies' ephemerides.
GeoMAJIS details for instance which objects are present in MAJIS' FOV,
their state (i.e position and velocity) with respect to different inertial
frames, the Sun's and its own position relative to them, and the
instrument' spatial resolution at their surface.
In addition, the program also performs ray-tracing computation specific
to each observation so that for each pixel, it might be known which
surface element of which object was observed, if it was fully illuminated
and visible, or shadowed or occulted from the spacecraft's perspective.
Such computations are performed either with a simple ellipsoidal
representation of the observed bodies, or, when available, also with
a detailled 3D model of the target.
All the results of the RTX computations are then written-out as a
multiple-frame FITS file, while the other results are written down into
the geometry dictionary of the PDS4 descriptor of the said FITS file,
complementing the corresponding scientific observation.

IV. The August 2024 Earth flyby
In complement to theoretical simulations, the GeoMAJIS program will be
tested and perfected in the shadow of the 20th of August 2024 Earth
flyby. While presently available ephemerids allow to simulate this
flyby, as illustrated by Fig. 1.

Fig. 1: 2D plot of the Local Time at the surface of the Earth, according the predicted trajectory of the JUICE spacecraft at the present time.
The plot depicts the simulation results of 196 acquisitions with the VISNIR channel (one every 15 minutes, vertical axis) taken with 400 pixels (horizontal axis).

As one can understand from the results of Fig.1, during this flyby, the spacecraft ingresses the Earth system in the shadow of the Earth and makes its egress while imaging most of the day-side and part of the evening-side terminator. The local time was evaluated and corrected by the sun's elevation with respects to the local normals of the surface.
Given the suite of instruments aboard the spacecraft, the reconstructed trajectory of the spacecraft will be computed with at best a sub-meter accuracy.
Such accuracy will allow to compare and find-out any differences between simulations and observations that might be due either to a theoretical 
or a factual origin.
These efforts will constitute a new evaluatory review of the program and its algorithm and any correction will be included in the eventual updated version of the calibration pipeline.

V. Conclusions
We designed, developped and tested a segment of MAJIS' calibration pipeline. This segment aims at providing the most complete description of the 
observational context of the varied MAJIS acquisitions.
We shall present the algorithm of this program, as well as the results of present simulations of observations in the Jupiter system as well as the preliminary 
results from the review of the August 2024 Earth flyby.

References:

• 1. Grasset et al., PSS, http://dx.doi.org/10.1016/j.pss.2012.12.002
• 2: Acton et al., Planet. Space Sci.. Vol. 44, No. 1, pp. 65-70. 1996

How to cite: Feller, C., Sophie, J., and Cédric, L.: GeoMAJIS, providing the observational context for the JUICE/MAJIS spectrometer, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-1073, https://doi.org/10.5194/epsc2024-1073, 2024.

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Asteroids are diverse objects found in the Solar System, populating in large numbers the near-Earth space, yet most of them form the main belt. While upcoming observatories promise frequent observations of asteroids to compute their orbit, understanding their physical composition remains a challenge due to the inherent difficulty in obtaining spectra.

Launched in 2013, GAIA satellite repeatedly scans the entire sky, not only mapping the more than 1 billion stars, but also discover and measure exoplanets, asteroids, comets and planetary satellites in our Solar System. For the asteroids and comets, GAIA provides more accurate astrometry, photometry as well as albedo and spectra.

In this study we accessed the freely available GAIA Solar System Objects reflectance spectra in the visible range (418-990 nm) and built a catalogue of their spectral wavelengths. After thresholding for the well calibrated spectras we tested 8 unsupervised machine learning (ML) models to cluster the spectras into classes.

After fine hyperparameter tuning of the models we found that several ML methods capture well the features of the already classified asteroids. Also, we obtained a spectral classification for the unclassified asteroids observed by GAIA.

The next step is to extend the study by employing the dynamical features and the albedo to better constrain the asteroid families, and ultimately, to understand distribution of the asteroids in the Solar System.

Acknowledgements: SA was supported during this work by the ESA Archival Research Visitor Programme.

How to cite: Anghel, S., Merin, B., and Kueppers, M.: Asteroid Classification with GAIA: Machine Learning Insights from Reflectance Spectra, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-1096, https://doi.org/10.5194/epsc2024-1096, 2024.

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Abstract:

Understanding the thermal properties of comets, asteroids, and icy moons is crucial for advancing our knowledge of their composition and evolution. An increasing number of missions to these bodies, including Rosetta, Osiris-Rex, Europa Clipper, and JUICE, necessitate thermal models that accurately consider their complex topographies.

We have developed a Python model that predicts diurnal temperature variations on airless bodies in three dimensions, factoring in their morphologies. This model significantly improves simulation accuracy by incorporating shadowing effects. The results are consistent with established models such as the one-dimensional thermal conduction model of J.R. Spencer, thermprojrs [1], and the three-dimensional surface energy balance model by Guilbert-Lepoutre & Jewitt [2].

Our tool was built to support missions such as the NASA Lucy Mission and the ESA Comet Interceptor mission. However, it is suited to a wide range of targets, including those with active surfaces. The model is user-friendly and operates quickly, running in seconds for basic shape models, and requiring only a few minutes for a shape model of comet 67P with 16,000 facets. It is designed to handle subsurface heating efficiently and is parallelised, enabling applications to large, high spatial resolution shape models.

Background:

The thermal behaviour of airless planetary bodies is governed by their surface and subsurface properties, including albedo, surface roughness, and thermal inertia. Accurate thermal models are essential for interpreting observational data and planning missions. Traditional models, such as Spencer's thermprojrs, have provided valuable insights into the thermal evolution of these bodies by simulating one-dimensional heat conduction. These models are instrumental in understanding the diurnal and seasonal temperature variations on the surface.

However, the complexity of three-dimensional topographic features, such as craters, ridges, and boulders, necessitates more advanced modelling techniques. While existing three-dimensional models have addressed some of these complexities, there remains a need for accessible, high-resolution models to simulate and interpret thermal data from current and upcoming missions accurately.

Outline of the Model:

Our open-source Python-based model aims to fill this gap by providing a comprehensive tool that can account for three-dimensional morphological features and shadowing effects. This model enhances the accuracy of temperature predictions and makes advanced thermal modelling techniques available to a wider scientific community. The integration of this model with thermal mapping data from missions can further refine shape models, leading to improved interpretations of the thermal properties of airless bodies.

The model solves a surface energy balance equation for each facet of the shape model file. This equation factors in solar heating, radiative loss, thermal conduction, and subsurface heating. The result is a temperature map that evolves over time, providing insights into the diurnal cycle and the thermal properties of the target body under various conditions.

This approach is efficient and user-friendly, requiring only a few minutes to process a shape model with thousands of facets. The model's parallelised nature allows for rapid computation, making it suitable for extensive simulations and analyses.

Validation Work:

To validate our model, we compared its output with the results from Spencer's one-dimensional model. As shown in Figure 1, the temperature versus time profiles from both models show close agreement, with slight differences attributed to the handling of deep layer temperatures.

Figure 1: Temperature vs Time for a single facet. Comparison between Spencer model and our model.

The model has been tested on various shape models, including comet 67P and the asteroid Bennu. The inclusion of shadowing effects, enhances the accuracy of temperature predictions in areas with significant topographic variation (Figure 2).

Figure 2: 3D illumination model with shadowing effects for shape models of a sphere, Bennu, and 67P.

The thermal map generated by our model (Figure 3), highlights the temperature distribution across the surface. These maps are essential for understanding the thermal evolution of the target bodies and planning mission operations. This model could also be used in tandem with thermal mapping data from missions to refine shape models, enhancing the accuracy of the thermal simulations and providing more detailed insights into the surface properties of the target bodies.

Figure 3: Thermal map showing temperature distribution on a sphere, Bennu, and 67P.

Conclusion:

Our open-source Python model represents an improvement in the simulation of thermal behaviour on airless planetary bodies. It provides accurate temperature predictions by incorporating three-dimensional surface features and shadowing effects. The model runs efficiently and can be parallelised, making it suitable for a wide range of applications, including realistic instrument simulations and mission planning.

Planned improvements to the model include implementing radiative self-heating, scattering of incident light, and energy loss through sublimation. Extensive parameter sensitivity analysis will further enhance the model's applicability to diverse targets.

The code is open source, and contributions are welcome:

github.com/duncanLyster/comet_nucleus_model

References:

• Spencer, J.R., Lebofsky, L.A., and Sykes, M.V., 1989. Systematic biases in radiometric diameter determinations. Icarus, 78(2), pp.337-354.
• Guilbert-Lepoutre, A., and Jewitt, D., 2011. Thermal shadows and compositional structure in comet nuclei. The Astrophysical Journal, 743(1), p.31.

How to cite: Lyster, D., Howett, C., and Penn, J.: Predicting Surface Temperatures on Airless Bodies: An Open-Source Python Tool, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-1121, https://doi.org/10.5194/epsc2024-1121, 2024.

EPSC2024-1121

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