Solar system

Abstract:

Clouds are the manifestations of atmospheric dynamics, chemistry, thermal evolution, and orbital characteristics; thus, understanding their physical and spectral properties and their spatial and temporal variability is critical to understanding the planet as a whole.  Observations of Saturn by the Hubble Space Telescope since 1994 and by Cassini from 2004 - 2017 have spanned almost one Saturn year (29.5 Earth years).  Despite the wealth of data, a self-consistent picture of the seasonal variations in Saturn’s haze and cloud structure remain elusive. In this work, we present a radiative transfer analysis of Cassini-VIMS (Visible and Infrared Mapping Spectrometer) spectra in order to derive the vertical structure and color properties of Saturn’s clouds and their latitudinal and seasonal variability.  VIMS records spectra over visible (0.3 to 1.05 micron) and infrared (0.85 to 5.1 micron) channels at spectral resolutions of 7 and 16 nm, respectively.  After a review of the VIMS dataset, we have identified dayside spectra that capture unique cloud features in a given latitude circle at multiple emission angles, allowing for improved vertical discrimination of cloud models. Data are additionally available over multiple epochs, allowing us to analyze any seasonal evolution.  Using the NEMESIS radiative transfer code (Irwin et al., 2008, JQSRT 109, 1136-1150), we invert the VIMS spectra to derive the vertical profiles of phosphine (PH3), ammonia (NH3) and the vertical structure of 4 haze/cloud layers (using the cloud model and cloud/gas parameters shown in Figure 1).  In preliminary findings, in adopting the chromophore optical constants derived by Sromovsky et al., 2021 (Icarus 362, 114409) for a north polar cloud observed in 2016, we find we can adequately fit the spectra for a subset of clouds observed in September 2014.  At other locations/times, the chromophore optical constants derived by Sromovsly et al., 2021, need to be varied in order to fit the spectra within uncertainty, which indicates seasonal evolution of Saturn’s chromophore.  In this work, we present derived cloud properties and the optical constants of the derived chromophore as a function of latitude and season in order to shed light on the complex interplay between cloud structure, color, chemistry, and orbital characteristics.     

Abstract:

On November 1, 2023, NASA’s Lucy spacecraft successfully imaged the Main-Belt asteroid (152830) Dinkinesh and its moon, Selam. Dinkinesh is an S- or Sq-type asteroid with multiple geologic features (i.e., craters, central ridge, and trough) [1].  The Dinkinesh system is complex, with satellite that itself is a contact binary [1]. Broadband visible (0.35-0.95 µm) and near-IR (0.97-3.95 µm) hyperspectral images collected by the L’Ralph instrument showed absorption features near 1-, 2-, and 3-µm [2, 3].  The vibrational absorption between 2.6 and 3.3 µm in asteroid spectra has generally been interpreted as OH and H2O (i.e., hydration). This ~3.0 µm band, has been a crucial tool of characterization to understand the degree of hydration on the surface of asteroids [4]. Detection of hydration or volatile-rich materials on S-type objects is surprising due to the expected high temperature at which these bodies formed in the main-belt and presence of anhydrous silicates. Ground-based facilities have provided crucial detections and insights about the 3.0 µm band on S-type asteroids [5,6], yet much remains unknown about its origin. Dinkinesh’s close approach by Lucy offers a fortuitous opportunity to better understand the hydration of these bodies and assess any spatial variation on the surface that might be related to geologic features. The Lucy L’Ralph Dinkinesh observations can help differentiate the source of hydration. For example, exogenous material (e.g., carbonaceous or cometary material) is expected to appear in discrete areas associated with specific surface features such as craters [7]. Alternatively, solar wind implantation on asteroids occurs when high H+ fluxes doses from the Sun interact with surface minerals, embedding hydrogen atoms and potentially leading to the formation of OH or H2O in the regolith [8]. We will report on the spectral analysis of Dinkinesh, with a focus on the shape model registration of hyperspectral images from the L’Ralph Multi-spectral Visible Imaging Camera (MVIC) and Linear Etalon Imaging Spectral Array (LEISA). We will present colors, spectral slopes, and band depth to look for possible spectral heterogeneities associated with geologic morphologies. Results: We registered the digital shape model of Dinkinesh to the L’Ralph instrument detectors. Figure 1 shows a preliminary example of the MVIC panchromatic filter frame during the close approach registered to the respective incidence angle backplane obtained using SpiceyPy [9]. Figure 2 shows an example of a LEISA-calibrated frame (e.g., I/F) registered to Dinkinesh’s shape model.  After registration, the 3 µm absorption feature is analyzed for each facet by computing the absorption strength (e.g., band depth) and looking for correlations with surface morphologies provided by stereophotogrammetry of L’LORRI images. Similarly, we obtained MVIC color maps and overlayed them on the shape model. Our preliminary analysis suggests a 3 µm detection across the entire imaged surface, showing variabilities in band depth. We will further explore such variability to find its possible relationship with surface morphologies, local color variations, and illumination geometry.Figure 1. MVIC panchromatic frame of Dinkinesh overlayed with the SpiceyPy incidence angle backplane.Figure 2. Left: Dinkinesh shape model with overlayed LEISA cross-track I/F frame 700 during close approach.  [1] Levison, H.F. et al. 2024. A contact binary satellite of the asteroid (152830)Dinkinesh. Nature 629, 1015–1020.[2] Simon, A. et al. 2025. Lucy L'Ralph In-flight Calibration and Results at (152830) Dinkinesh. Planet. Sci. J.  6, 7.[3] Kaplan, H., et al. 2024.  "Multi-spectral imaging observations of asteroid (152830) Dinkinesh by the Lucy Mission." Proceedings of the Lunar and Planetary Science Conference 2024,abstract #1474. Houston, TX: Lunar and Planetary Institute.[4] Rivkin, A. S. et al. 2018. Evidence for OH or H2O on thesurface of 433 Eros and 1036 Ganymed. Icarus 304, 74–82.[5] McGraw, L. E. et al. 2022. 3 μm Spectroscopic Survey of Near-Earth Asteroids. Planet. Sci. J. 3, 243.[6] McAdam, M. et al. 2024. Detection of Hydration on Nominally Anhydrous S-complex Main Belt Asteroids. Planet. Sci. J. 5, 254.[7] De Sanctis, M. C. et al. 2015. Mineralogy of Marcia, the youngest large crater of Vesta: Character and distribution of pyroxenes and hydrated material. Icarus 248, 392–406.[8] Hibbits, C. A., et al., 2011. Thermal stability of water and hydroxyl on the surface of the Moon from temperature-programmed desorption measurements of lunar analog materials. Icarus, 213, 64-72.[9] Annex, A. M., et al., 2020. SpiceyPy: a Pythonic Wrapper for the SPICE Toolkit. Journal of Open Source Software, 46, 2050.

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:

Larger-than-Earth exoplanets are sculpted by strong stellar irradiation, but it is unknown whence they originate. Two propositions are that they formed with rocky interiors and hydrogen-rich envelopes (‘gas-dwarfs’), or with bulk compositions rich in water-ices (‘water-worlds’) . Multiple observations of super-Earth L 98-59 d have revealed its low bulk-density, consistent with substantial volatile content alongside a rocky/metallic interior, and recent JWST spectroscopy evidences a high mean molecular weight atmosphere. Its density and composition make it a waymarker for disentangling the processes which separate super-Earths and sub-Neptunes across geological timescales. We simulate the possible pathways for L 98-59 d from birth up to the present day using a comprehensive evolutionary modelling framework. Emerging from our calculations is a novel self-limiting mechanism between radiative cooling, tidal heating, and mantle rheology, which we term the 'radiation-tide-rheology feedback'. Coupled numerical modelling yields self-limiting tidal heating estimates that are up to two orders of magnitude lower than previous calculations, and yet are still large enough to enable the extension of primordial magma oceans to Gyr timescales. Our analysis indicates that the planet formed with a large amount (>1.8 mass%) of sulfur and hydrogen, and a chemically-reducing mantle; inconsistent with both the canonical gas-dwarf and water-world scenarios. A thick atmosphere and tidal heating sustain a permanent deep magma ocean, allowing the dissolution and retention of volatiles within its mantle. Transmission features can be explained by in-situ photochemical production of SO2 in a high-molecular weight H2-H2S background. These results subvert the emerging gas-dwarf vs. water-world dichotomy of small planet categorisation, inviting a more nuanced classification framework. We show that interactions between planetary interiors and atmospheres shape their observable characteristics over billions of years.

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.