Planetary surfaces

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:

Introduction: Understanding surface temperatures on airless planetary bodies is crucial for interpreting thermal observations and constraining surface properties. We present TEMPEST (Thermal Evolution Model for Planetary Environment Surface Temperatures), a modular, open-source Python model that simulates diurnal and non-periodic thermal evolution on irregular bodies. Unlike traditional 1D periodic solvers, TEMPEST handles transient heating events such as eclipses, non-synchronous rotations such as tumbling asteroids, and seasonal variations. Key capabilities include surface roughness modelling via hemispherical craters, multiple thermal conduction schemes, and modular scattering using lookup tables (LUTs). TEMPEST has been used to analyse data from the Lucy mission and has been validated against the well-established Spencer 1D thermal model, thermprojrs [1].Figure 1: TEMPEST allows the user to select a facet to view any of its time varying properties including insolation, temperature and radiance. The diurnal temperature curves (right) are those of the corresponding outlined facets selected by the user in the interactive pane (left).Methods: TEMPEST calculates surface temperatures by solving a surface energy balance that includes solar flux, thermal emission, vertical heat conduction, and (optionally) radiative self-heating. Figure 1 shows the user interface once the model has completed a run. Key components include:Thermal solvers: Includes a standard 1D periodic conduction scheme influenced by the widely used thermprojrs [1] and a non-equilibrium solver, designed for better performance and stability in non-periodic cases. Scattering treatments: Utilises precomputed LUTs for various scattering laws (e.g., Lambertian, Lommel-Seeliger). This structure allows users to incorporate empirical bi-directional reflectance function (BRDF) data (e.g., from goniometer measurements of lunar regolith) or test the impact of different scattering assumptions, which can be particularly important for investigating the temperature of shadowed regions, as shown in Figure 2. The modularity also facilitates user modification for specific research needs. Surface roughness: Implemented via hemispherical sub-facet craters with adjustable rim angle to match roughness with a specified RMS slope angle. Non-periodic and time-dependent conditions: Supports time-dependent boundary conditions, including periodic scenarios such as eclipses and seasonal variations due to orbital eccentricity, as well as non-periodic cases including tumbling rotation, endogenic heating, and, or other user-defined transient heating scenarios. Designed for efficient parallel execution, the model runs effectively on multi-core personal computers and can efficiently simulate shape models with tens of thousands of facets. It has also been deployed on high-performance computing clusters for larger-scale models on the order of 1 million facets. Input configuration files are simple and flexible, allowing integration into larger analysis pipelines.Figure 2: An example insolation curve from a 1666 facet model of the bilobate comet 67P. The effects of scattered light can be seen either side of the main peak, this is particularly important for permanently shadowed regions. The selected facet is shown with a blue outline; sunlight direction is shown with a yellow arrow.Results: We validated TEMPEST by comparing temperature time series with Spencer’s 1D model thermprojrs [1] under idealised conditions, showing consistent results – see Figure 3. Applied to high-resolution shape models of 67P/Churyumov-Gerasimenko and 101955 Bennu, the model produces detailed temperature maps that reflect the significant influence of self-shadowing and local geometry, quantifying, for example, the temperature reduction in shadowed craters. Non-periodic simulations have been run to explore rotational transitions and eclipse effects, enabling new modes of comparison with observational datasets. The modular scattering and roughness components offer a powerful way to assess how sub-resolution scale parameters impact apparent thermal inertia and surface radiative behaviour. TEMPEST is already being used to interpret thermal data from recent missions, including Lucy, and can be adapted for upcoming datasets from targets like those of Comet Interceptor and Europa Clipper.Figure 3: TEMPEST shows good agreement with ‘industry standard’ thermophysical models in 1 dimension.TEMPEST is open-source and available at:github.com/duncanLyster/TEMPEST/Acknowledgement: This work was made possible by support from the UK Science and Technology Facilities Council. References:[1] Spencer, J.R., Lebofsky, L.A., and Sykes, M.V., 1989. Systematic biases in radiometric diameter determinations. Icarus, 78(2), pp.337-354.[2] Lyster, D., Howett, C., & Penn, J. (2024). Predicting surface temperatures on airless bodies: An open-source Python tool. EPSC Abstracts, 18, EPSC2024-1121.[3] Lyster, D.G., Howett, C.J.A., Spencer, J.R., Emery, J.P., Byron, B., et al. (2025). Thermal Modelling of the Flyby of Binary Main Belt Asteroid (152830) Dinkinesh by NASA’s Lucy Mission. Submitted to EPSC Abstracts, 2025.

Abstract:

Introduction: The Lucy mission's first asteroid flyby provided a unique and unexpected opportunity to study a binary asteroid system up close. Originally expected to encounter a single target, Dinkinesh, the discovery of its small, tidally locked moon, Selam, introduced additional opportunity and complexity to the interpreting flyby observations [1]. We present thermal modelling of the binary system, quantifying how the presence of Selam influenced radiance measurements and indicating its possible impact on thermal inertia estimates. Thermal inertia (TI) offers insight into surface properties such as grain size and regolith structure. Determining the TI of Dinkinesh adds to our understanding of small S-type asteroids and enables comparison within a binary, potentially revealing differences driven by tidal effects or surface evolution.Methods: We modelled the flyby geometry and instrument measurements using the new TESBY (Thermal Emissions Spectrometer flyBY) module of TEMPEST (the Thermophysical Equilibrium Model for Planetary Environment Surface Temperatures) [2] to simulate the thermal radiance of both bodies and assess their combined effect on interpretation of data from the Lucy Thermal Emission Spectrometer (L’TES) instrument [3].The Thermal Model: Dinkinesh and its satellite, Selam, were modelled in TEMPEST. A stereo-photogrammetric shape model is available for the primary target – Dinkinesh [4], with ~2 m lateral and ~0.5 m vertical resolution, covering ~60% of the surface. This shape model was downsampled to a dimensionally accurate model with 1266 facets with a resolution of ~35 m. A sphere of representative diameter (230 m [1]) was used for the satellite Selam.Figure 1: TESBY visualization of flyby. Global view of the flyby trajectory (left), and the FOV of the instrument (centre), with corresponding L’LORRI image for comparison [1] taken 0.54 minutes before closest approach (right). Input is the TEMPEST [5] result for the shape model of Dinkinesh, and representative diameter sphere for Selam. Parameters used: solar distance = 2.19 AU, rotation periods = 3.74 hours (Dinkinesh) and 52.7 hours (Selam) [1] thermal inertia (provisional) = 40 J m-2 s-1/2 K-1, geometric albedo = 0.27Flyby geometry: Building on the TEMPEST framework, the TESBY module is given the geometry information for the flyby and the thermal data from TEMPEST. Based on the 7.3 mrad Field-of-View (FOV) of the L’TES instrument [3] TESBY produces simulated radiance measurements by computing a weighted sum of blackbody curves from each visible facet, based on its temperature, projected area, and emission angle. Matching these modelled radiances to the instrument data allows us to fit for the thermal inertia of the asteroid. A complicating factor in this study is that the sensitivity of L’TES is not uniform across its FOV, including this effect in the model is the subject of ongoing work.Figure 2: Preliminary modelled radiance results (blue line) compared to L’TES observation (red) using the same model settings as Fig. 1. Scaled radiances (dotted line) are also provided (see main text for more information).Results: An example of the currently predicted model radiance is given by Figure 2. As it shows, there is a notable offset between the predicted and observed radiances. Accounting for the position of the targets in the L’TES FOV is expected to resolve the observed discrepancy in absolute radiance levels. However, as the scaled model shows, the predicted radiances are able to capture the shape of the L’TES radiance.We find that due to the slower rotation rate of Selam, the maximum surface temperatures on the satellite can be as much as 25 K higher than those on Dinkinesh (Fig. 1), meaning that despite the small size (lobe diameter of only 230 m, compared with 790 m for Dinkinesh [1]), the contribution to measured radiance is significant. This effect is highlighted by investigation of the integrated radiances of the targets throughout the flyby (Fig. 3), where the entry and exit of Selam within the FOV is visible, as well as the dip in integrated radiance while Selam is partially eclipsed by Dinkinesh. Our results demonstrate the importance of considering the full system in flyby analysis, informing techniques for similar encounters in the future. This work highlights how the thermal signature of even a small secondary body can significantly impact observations, shaping our understanding of asteroid surface properties and thermal environments.Continued analysis will focus on the use of TEMPEST/TESBY to constrain the thermal inertia of this binary asteroid from L’TES flyby observations.  Figure 3: Variation in integrated wavelength for Dinkinesh (target, blue), Selam (satellite, red) and combined effect (green). Radiances were integrated over the 200–1500 cm⁻¹ spectral range. The results show that despite its small size, Selam makes a significant difference to the spectral radiance, particularly at shorter wavelengths. The dip in combined spectral radiance at observations 3315-3320 is due to Selam being eclipsed by Dinkinesh.The thermal model code is open source and available at: github.com/duncanLyster/TEMPEST/Acknowledgement: This work was made possible by support from the UK Science and Technology Facilities Council.  References: [1] Levison, H.F., Marchi, S., Noll, K.S. et al. A contact binary satellite of the asteroid (152830) Dinkinesh. Nature 629, 1015–1020 (2024).[2] Lyster, D., Howett, C., & Penn, J. (2024). Predicting surface temperatures on airless bodies: An open-source Python tool. EPSC Abstracts, 18, EPSC2024-1121.[3] Christensen, P. R., et al. The Lucy Thermal Emission Spectrometer (L’TES) Instrument, Space Sci. Rev. (2023)[4] Preusker, F. et al. (2024). Shape Model of Asteroid (152830) Dinkinesh from Photogrammetric Analysis of Lucy’s Frame Camera L’LORRI. 55th Lunar and Planetary Science Conference, Abstract #1903.[5] Lyster, D., Howett, C., & Penn, J. (2025). TEMPEST: A Modular Thermophysical Model for Airless Bodies with Support for Surface Roughness and Non-Periodic Heating. Submitted to EPSC Abstracts, 2025

Abstract:

. IntroductionPerhaps one of the most fascinating ice-covered moons in our solar system is the Galilean satellite Europa. The successful launch of Europa Clipper has motivated the re-evaluation of our current knowledge of the Jovian moon -- specifically thermal measurements of the moon's surface, which may contain information about recent geologic activity. After the discovery of active plumes on Enceladus [1], similar phenomena were searched for on Europa [2]. While evidence of surface alteration -- such as troughs, ridges, chaos terrain, and the lack of prevalent craters -- indicate ongoing activity and a relatively young surface [3], the presence of plumes is still being debated. While no endogenic thermal anomalies have yet been observed on Europa's surface [4], we re-assess the thermal IR data from Galileo Orbiter's photopolarimeter-radiometer instrument (PPR) [5]. We perform a thermal analysis of the surface properties of Europa, including mapping the thermal inertia and albedo similar to what was done by Rathbun et al. [4], with a goal of extending thermal surface mapping beyond the previous 20% surface coverage. We also perform a sensitivity study of PPR in hotspot detection by determining the minimum detectable hotspot temperature across the surface of the moon and compare our results to previous work. 2. Data AnalysisWe use 29 PPR radiometry datasets taken during various orbits ranging from November 1996 to November 1999. Both narrow band and open filters were used, with a total wavelength range of 0.3-110 μm. We divide the surface into 3°x3° longitude/latitude grid cells and determine each cell's temperature at a given local time to produce diurnal temperature curves. To determine the thermal inertia and albedo, we fit a thermophysical model to each cell's diurnal curve using the Thermophysical Body Model Simulation Script (TEMPEST) [6] as our modelling tool. The best-fit diurnal curve is chosen by minimizing the reduced chi-squared of the model fit, while all data with χred2

Abstract:

NASA’s New Horizons spacecraft continues to explore the Kuiper belt after its historic close flybys of the Pluto system in 2015 at ~33 astronomical units (AU) [1] and the cold classical Kuiper belt object (KBO) Arrokoth in 2019 at ~43 AU [2].  New Horizons is located at ~61.7 AU as of this writing in May 2025, and travels about 3 AU per year.  New Horizons has sufficient power, propellant, and communications capability to continue operations until the mid-to-late 2040s and, thus, should be able to collect data out to distances of ~120 AU or greater. In its extended mission, New Horizons’ main planetary science focus is studying Kuiper belt dwarf planets and small KBOs, and their environment.  We will provide an overview of results for the dwarf planets and smaller KBOs observed by New Horizons from a distance ([3-6]; also see Porter et al., 2025 abstract at this conference).  New Horizons can observe KBOs from much higher phase angles than possible from Earth, and some of the observed KBOs come as close as 0.1 AU to the spacecraft.  This allows for the study of shapes, poles, surface properties, and  searches for close satellites in some cases.  New Horizons also made high-phase, color observations of the ice giants Uranus and Neptune [7] in coordination with the Hubble Space Telescope and as an exoplanet observation analogue.  Further, the New Horizons Student Dust Counter continues to observe elevated dust fluxes at larger distances than expected, and the team is exploring possible explanations for why the dust flux has not yet started to decrease as predicted by previous models [8, 9].  We will also highlight some new products and findings related to Arrokoth, including a new shape model [10], images draped onto the shape model, and a study placing Arrokoth’s crater size-frequency distribution into the context of those on other small bodies [11].  Looking towards the future of New Horizons: We will provide a status update on the ground-based, Subaru Telescope search [12-14] for a future close flyby target and other KBOs that New Horizons could observe as point sources.  We will also discuss how future work would enhance the chances of finding a future flyby target for New Horizons, including the additional use of machine learning/artificial intelligence, supercomputing, and potential observations from the Vera Rubin Observatory (also see Kavelaars et al. 2025 abstract in this conference) or the Roman Space Telescope.References:[1]  Stern S. A. et al., 2015, The Pluto system: Initial results from its exploration by New Horizons, Science 350, id.aad1815. doi:10.1126/science.aad1815[2]  Stern S. A. et al., 2019, Initial results from the New Horizons exploration of 2014 MU69, a small Kuiper Belt object, Science 364. doi:10.1126/science.aaw9771[3]  Verbiscer A. J. et al., 2024, The New Horizons Photometric Phase Angle Survey of Deep Outer Solar System Objects: From the Kuiper Belt to the Scattered Disk, 55th Lunar and Planetary Science Conference. 3040, 2531.[4]  Verbiscer A. J. et al., 2022, The Diverse Shapes of Dwarf Planet and Large KBO Phase Curves Observed from New Horizons, The Planetary Science Journal 3, 95. doi:10.3847/PSJ/ac63a6[5]  Verbiscer A. J. et al., 2019, Phase Curves from the Kuiper Belt: Photometric Properties of Distant Kuiper Belt Objects Observed by New Horizons, Astron. J. 158. doi:10.3847/1538-3881/ab3211[6]  Porter S. B. et al., 2016, The First High-phase Observations of a KBO: New Horizons Imaging of (15810) 1994 JR1 from the Kuiper Belt, ApJ Letters 828. doi:10.3847/2041-8205/828/2/L15[7]  Hasler S. N. et al., 2024, Observations of Uranus at High Phase Angle as Seen by New Horizons, The Planetary Science Journal 5, 267. doi:10.3847/PSJ/ad8cdb[8]  Corbett T. et al., 2025, Production, Transport, and Destruction of Dust in the Kuiper Belt: The Effects of Refractory and Volatile Grain Compositions, Astrophys J. 979, L50. doi:10.3847/2041-8213/adab75[9]  Doner A. et al., 2024, New Horizons Venetia Burney Student Dust Counter Observes Higher than Expected Fluxes Approaching 60 AU, pp. arXiv:2401.01230.[10]  Porter S. B. et al., 2024, The Shape and Formation of Arrokoth, 55th Lunar and Planetary Science Conference. 3040, 2332.[11]  Knudsen I. E. et al., 2024, An Analysis of Impact Craters on Small Bodies Throughout the Solar System, The Trans-neptunian Solar System.[12]  Yoshida F. et al., 2024, A deep analysis for New Horizons' KBO search images, Publications of the Astronomical Society of Japan 76, 720-732. doi:10.1093/pasj/psae043[13]  Fraser W. C. et al., 2024, Candidate Distant Trans-Neptunian Objects Detected by the New Horizons Subaru TNO Survey, The Planetary Science Journal 5, 227. doi:10.3847/PSJ/ad6f9e[14]  Buie M. W. et al., 2024, The New Horizons Extended Mission Target: Arrokoth Search and Discovery, The Planetary Science Journal 5, 196. doi:10.3847/PSJ/ad676d