Planet

The Moon’s ancient magnetic field provides critical insights into its thermal and magnetic evolution, yet the lifetime of its dynamo remains debated. Returned samples yield complex and contradictory paleomagnetic records, while orbital data reveal crustal magnetic anomalies of uncertain origin from either a core dynamo or transient impact-generated fields. Here we jointly invert gravity and magnetic observations in the region around the Dewar swirl, a high-albedo feature associated with the Dewar magnetic anomaly. We identify a shallow, magnetized, high-density body consistent with buried mare basalt. Its formation requires paleointensity exceeding 11 μT, suggesting a lunar dynamo was active at about 4.2 Ga, as constrained by the superposed basin ejecta. Results also show that swirl formation requires horizontal magnetization and iron oxide enrichment. These findings link a magnetic anomaly to its geologic source and the state of the lunar dynamo, providing new constraints on the lunar magnetic and volcanic history.

We present the LunarLeaper mission concept, which aims to robotically investigate volcanic pits on the lunar surface. Volcanic pits, or skylights, are collapse features that may provide access to subsurface lava tubes, which could serve as shelters for future human explorers and offer insight into the volcanic history of the Moon by exposing ancient lava flows. The existence and extent of large caves are still debated today and require in situ analysis. The Marius Hills site in particular offers a potential entry point to a cave system in a volcanic region on the lunar nearside. Our mission aims to deploy a payload-equipped 15kg-class legged robot that can approach a pit, such as the Marius Hills pit, while taking measurements during the traverse. During the mission, measurements from a ground-penetrating radar (GPR) and a gravimeter will allow us to survey the subsurface and map any underlying lava tube, if present. The mission will investigate key questions regarding lunar volcanism, such as the existence and geometry of subsurface caves and the magnitude and timing of lava flows, while assessing the site’s suitability for future human utilization and habitation. Furthermore, the mission will demonstrate key enabling technologies such as legged robots, serving as building blocks for the next generation of planetary missions. • The LunarLeaper mission concept proposes a 15kg-class legged robot to investigate the existance of lunar lava tubes and volcanic pits. • Autonomous, legged robotic exploration enables geophysical surveys and high-resolution imaging of the Marius Hills Pit within one lunar day. • Legged locomotion technology demonstrates enhanced mobility for traversing complex lunar terrain.

ContextThe VenSpec-U instrument[1] onboard the future ESA EnVision mission, will be one of the three spectrometers of the VenSpec suite, that will study Venus’ surface and atmosphere. VenSpec-U will focus on the upper part of the atmosphere and will perform UV observations of the cloud top. It will measure the backscattered sunlight on the dayside of Venus, from which radiance factor spectra will be derived by taking the incoming solar spectral irradiance into account. The scientific objectives of VenSpec-U will regard both the chemical composition and the dynamical properties of the atmosphere. Four main elements to investigate can be derived from these science goals. The firsts are the abundance of sulphured species, including especially SO2 and SO. These species will be identified separately thanks to the “High Resolution” (HR) channel of the instrument, that will have a spectral resolution of 0.3 nm to cover the 205-235 nm wavelength range corresponding to the common absorption band of SO2 and SO. The third objective is to monitor the unidentified UV absorber and its spectral characteristics. To that end, the “Low Resolution” (LR) channel of the instrument will operate from 190 nm to 380 nm, with a spectral resolution of 2-5 nm. Finally, VenSpec-U will investigate the small scale spatial variability of the cloud-top, by performing observations with a pushbroom observation strategy that allows the acquisition of images, at spatial samplings ranging from 3 to 24 km. Objective and methodThis study is focused on the imaging capabilities of VenSpec-U, in order to estimate the performances of the instrument for the retrieval of the small scale spatial features of Venus’ atmosphere, such as convection cells and atmospheric wave patterns. To do so, several numerical models are combined in order to simulate images as produced by VenSpec-U. The whole acquisition process aims to be modelled, from the observed scene at the cloud-top of Venus, to the consideration of the instrument’s behaviour inducing degradations of the images and spectra. The simulation of the observed scene is based on a mesoscale model of Venus’ atmosphere[2], from which maps of the key parameters that aimed to be monitored by VenSpec-U are derived, and radiance factor spectra are generated using a radiative transfer model adapted to the instrument’s wavelength range[3]. An instrumental model has then been developed in order to simulate the formation of images, by following successively the main steps occurring in the process, including the effects of optical components and detector as well as the main onboard data processing operations. A simple inversion algorithm is then applied. This latter is based on the simulation of a solar observation, which is foreseen for the inflight calibration of VenSpec-U, coupled to the inverse radiative transfer model that allow to process the degraded spectra to retrieve the maps of atmospheric parameters. These maps can then be compared to the initial maps used as inputs of the model binned to match the targeted spatial sampling (Fig. 1).By modifying the amplitude of the spatial features on the initial maps, detection threshold for each parameter can be estimated so that the compliance with the targeted performances in terms of small scale variability imaging can be assessed (Fig. 2). This study has been performed for optimal observation conditions, but could however be repeated for various configurations in order to estimate their impact on the resulting detection thresholds and global performances of VenSpec-U as an imager.Figure 1: Comparison between initial (top) and retrieved maps (bottom) of the three atmospheric parameters targeted by the LR channel: SO2 mixing ratio (left), UV absorber imaginary refractive index (center), cloud-top control altitude (right)Figure 2: Evolution of the correlation between the initial and retrieved maps of the four atmospheric parameters targeted by VenSpec-U: SO2 mixing ratio (top left), UV absorber imaginary refractive index (top right), cloud-top control altitude (bottom left), SO/SO2 abundance ratio (bottom right) References[1] Emmanuel Marcq, Franck Montmessin, Jérémie Lasue, Bruno Bézard, Kandis L. Jessup, et al.. Instrumental requirements for the study of Venus’ cloud top using the UV imaging spectrometer VeSUV. Advances in Space Research, 2021, 68 (1), pp.275-291. ⟨10.1016/j.asr.2021.03.012⟩. ⟨insu-03179739⟩[2] Lefèvre, M., Lefèvre, F., Marcq, E., Määttänen, A., Stolzenbach, A., & Streel, N. (2024). Impact of the turbulent vertical mixing on chemical and cloud species in the Venus cloud layer. Geophysical Research Letters, 51, e2024GL108771. https://doi.org/10.1029/2024GL108771[3] Lucile Conan, Emmanuel Marcq, Benjamin Lustrement, Nicolas Rouanet, Léna Parc, et al.. The VenSpec-U spectrometer onboard EnVision: sensitivity studies. Infrared Remote Sensing and Instrumentation XXXII. Proceedings SPIE 13144, Aug 2024, San Diego, United States. 43 p., ⟨10.1117/12.3027500⟩. ⟨insu-04733439⟩

Introduction:On Mercury, faculae are high-albedo, spectrally red, deposits originating from explosive volcanic eruptions (Kerber et al., 2009; Goudge et al., 2014; Thomas et al., 2014) whose source are likely rimless depressions. These depressions are usually located in the center of the facula and interpreted to be volcanic vents. In this work we analyzed the Agwo Facula and Abeeso Facula, sited in the western margin of Caloris basin (Fig.1) (22.39°N, 146.16°E and 21.82°N, 145.29E, respectively), performing a high resolution geomorphological map of both the sites.Fig.1 Left panel: Location of Agwo Facula and Abeeso Facula; right panel: Enlarged view of the area of interest showing the different derived mosaics overlapping the MDIS BDR basemap. Data and methods:In order to compile the maps of the two faculae we used MDIS (MESSENGER Mercury Dual Imaging System) mosaics. In particular, for Agwo Facula the main basemap is represented by derived mosaics with a spatial resolution ranging from 20 m/pixel to 28 m/pixel and with different illumination conditions. Additionally, a BDR (Basemap reduced Data Record) MDIS mosaic, with a resolution of 166 m/pixel, was considered. MDIS Wide Angle Camera (WAC) color maps, based on the reflectance at 750 nm and the visible spectral slope between 480 and 830 nm, respectively, were also used as part of the analysis. These latter maps helped determine the areal extent of the pyroclastic deposits. Finally, a Digital Terrain Model (DTM) of the region, derived from MDIS images using the technique of stereophotoclinometry (SPC) with a spatial resolution of 20 m/pixel, helped us to better characterize the facula’s topography.For Abeeso Facula the main basemaps were MDIS derived mosaics with a spatial resolution ranging from 25 to 56 m/pixel and with different illumination conditions. Also for this area, the BDR MDIS mosaic (166 m/pixel) and MDIS WAC color maps were used as ancillary basemaps.Results:The geomorphological maps highlight that both Agwo Facula and Abeeso Facula experienced several explosive episodes. In particular for Agwo, through the cross-cutting relationship observed among the pits, we recognized at least eight eruptive events. The terrain within the pits shows different surface texture and albedo that allowed the distinction of several geological units: from the older and smoother surfaces to the younger and rougher textured surfaces.On the other hand, Abeeso Facula experienced at least four different explosive episodes: the western pit is the largest and oldest one, with a smoother surface texture, whereas the eastern pit is the smallest of the cluster showing a crisper morphology, that suggests a younger age.Moreover, differently from Agwo, Abeeso shows several hollows, located in the eastern and north-western section of the perimeter of the depression.Conclusion:The morphological and spectral characteristics of pits suggest that both Agwo Facula and Abeeso Facula are the result of multiple eruptions, which likely occurred at different times. However, apart from their similarities, Abeeso shows hollow features not observed on Agwo. This, despite their proximity, suggests a different composition of the substrate hosting the faculae.

Abstract Hollows on Mercury are small (hundreds of meters ‐ few kilometers), shallow (tens of meters), irregular depressions typically found in clusters, often associated with impact craters, and likely formed by the loss of volatile materials. While their exact formation process remains debated, various hypotheses suggest sublimation or space weathering. In this study, we analyzed the global distribution of hollows, exploring their spatial patterns and relationships with key geological features. Our findings challenge the idea that hollows arise from a single volatile‐rich surface layer, suggesting instead that volatiles are dispersed throughout the crust. Hollows show no correlation with specific geological units or elevations, indicating no singular volatile source. Moreover, the transitory nature of hollows is suggested as they are rare in older, degraded craters but common in younger ones or older craters with deep‐seated features, hinting at a link to the reworking of materials through impacts or volcano‐tectonic activity.

Cereal crops play a critical role in global food security, but their productivity is increasingly threatened by climate change. This study evaluates the feasibility of using PlanetScope satellite imagery and a UAV equipped with the MicaSense RedEdge multispectral imaging sensor in monitoring winter wheat under various fertilizer treatments in a Mediterranean climate. Eleven fertilizer treatments, including organic-mineral fertilizer (OMF) pellets, were tested. The results show that conventional inorganic fertilization provided the highest yield (8618 kg ha⁻1), while yields from OMF showed a comparable performance to traditional fertilizers, indicating their potential for sustainable agriculture. PlanetScope data demonstrated moderate accuracy in predicting canopy cover (R2 = 0.68), crop yield (R2 = 0.54), and grain quality parameters such as protein content (R2 = 0.49), starch (R2 = 0.56), and hectoliter weight (R2 = 0.51). However, its coarser resolution limited its ability to capture finer treatment-induced variability. MicaSense, despite its higher spatial resolution, performed poorly in predicting crop components, with R2 values below 0.35 for yield and protein content. This study highlights the complementary use of remote sensing technologies to optimize wheat management and support climate-resilient agriculture through the integration of sustainable fertilization strategies.

CUBES (Cassegrain U-Band Efficient Spectrograph) is designed to be the most efficient UV spectrograph on an 8-10m class telescope, exploiting the higher telescope efficiency relative to the ESO ELT in ground-based UV wavelengths. The instrument provides wavelength coverage over a range of 300-405nm, with high instrumental efficiency (>37%) and a resolving power of >20,000 (HR) and >6,000 (LR). It will be mounted on the ESO VLT in Paranal. This paper presents the finalised optical design of the system as the project approaches the final design review. The wavelength range, Cassegrain mounting, high-resolving power, and efficiency requirements introduce some unique design challenges and complexities, detailed in the various optical subsystems including: a UV-optimized atmospheric dispersion corrector; high-line density ruled gratings; a dispersion-compensating spectrographic camera design; and an unusual active flexure compensation system.

The MErcury Radiometer and Thermal infrared Imaging Spectrometer (MERTIS) is part of the payload of the Mercury Planetary Orbiter spacecraft of the ESA-JAXA BepiColombo mission. MERTIS combines an imaging spectrometer covering the wavelength range of 7-14 μm with a radiometer covering the wavelength range of 7-40 μm. The instrument will map the whole surface of Mercury with a spatial resolution of 500 m for the spectrometer channel and 2 km for the radiometer channel. The compositional map of Mercury provided by MERTIS will allow unique insights into the evolution of the least explored terrestrial planet and will directly address questions raised by the NASA MESSENGER mission. For example, MERTIS will be able to provide spatially resolved compositional information on the hollows and pyroclastic deposits and answer the question whether hollows are actually predominately composed of sulfide. MERTIS will also provide spatially resolved temperature maps inside the permanently shadowed craters, thereby potentially constraining the stability of water ice deposits in those craters.BepiColombo is currently in the final part of its 7-year journey to Mercury. The interplanetary cruise includes in total nine flybys for gravitational assists: one at Earth, two at Venus and six at Mercury. MERTIS could obtain so far observations during the Earth flyby in April 2020, the first Venus flyby (FB1) in October 2020 and the second Venus flyby (FB2) on August 10, 2021. The recently published results for FB2 show that MERTIS performed well beyond requirements and provided new insights into the long-term stability of the Venusian atmosphere.

IntroductionSimulating thermal convection in Earth and planetary interiors is crucial for various applications, including benchmarking numerical codes (e.g. [1], [2]), deriving scaling laws for heat transfer in complex flows (e.g. [3], [4]), determining mixing efficiency (e.g. [5], [6]) and the characteristic spatial wavelengths of convection (e.g. [7]). However, reaching a statistically-steady state in these simulations, even in 2D, can be computationally expensive. While choosing "close-enough" initial conditions can significantly speed simulations up, this selection process can be challenging, especially for systems with multiple controlling parameters. This work explores how machine learning can be leveraged to identify optimal initial conditions, ultimately accelerating numerical convection simulations on their path to a statistically-steady state.Convection model and neural network architectureWe have compiled a comprehensive dataset comprising 128 simulations of Rayleigh-Bénard convection within a rectangular domain of aspect ratio 4, characterized by variable viscosity and internal heating. A randomized parameter sweep was employed to vary three key factors: the ratio of internal heat generation to thermal driving forces (RaQ/Ra), the temperature-dependent viscosity contrast (FKT), and the pressure-dependent viscosity contrast (FKV). Each simulation was executed until the root mean square velocity and mean temperature attained a statistically stationary state. Subsequently, we extracted the time-averaged one-dimensional temperature profile, horizontally averaged across the domain, over the final 200 time steps of each simulation.Figure 1: Schematic of the neural network used to predict statistically-steady temperature profiles.We used a feedforward neural network (NN) to predict the 1D temperature profiles as a function of the control parameters (Fig. 1). We modified the formulation of the NN from the one we used in [8] in the following ways. First, instead of each training example consisting of the full temperature profile, we now predict temperature at a given spatial point in depth (y). This allows the network to learn and predict profiles on variable computational grids, making it more adaptable. This formulation also avoids the "wiggles" encountered when predicting high-dimensional profiles. Second, we use skip-connections. For a given hidden layer, the features from all the previous hidden layers are added to it before applying the activation function. Third, we use SELU instead of tanh for faster convergence. Fourth, we concatenate the parameters to the last hidden layer, a technique shown to improve learning in some cases  (e.g., [9]). Fifth, as a consequence of conditioning the NN on spatial points, we augment the data with copies of points from the bottom and top thermal boundary layers to ensure that each batch is likely to be more representative of relevant temperature features occurring at the outer boundaries of the domain. We split the dataset into training, cross-validation, and test sets with 96, 15, and 16 simulations, respectively. To test the extrapolation capacity of the network, we pick simulations for the test set only where at least one of the simulation parameters exceeds a certain threshold. The cross-validation dataset consists of interpolated values only and is used additionally to choose the best performing NN architecture and save network weights only when the cross-validation loss improves. ResultsWe test the NN against the following baselines: linear regression, kernel ridge regression, and nearest neighbor. As shown in Table 1, when predicting unseen profiles from interpolated cases in the cross-validation set or even the extrapolated cases (Test set), the NN predictions are the most accurate among the tested possibilities. We visualize some of the profiles predicted by these algorithms in the cross-validation set in Fig. 2. Linear regression results are left out for ease of visualization.  Algorithm                  Training set Cross-validation set Test set Linear Regression  0.0385 0.0388 0.0676 Kernel Ridge Regression  0.0148 0.0147 0.0371 Neural Network 0.0071  0.0071  0.0187 Nearest Neighbor   0.0 0.0282  0.0495 Table 1: Mean absolute error of the prediction of 1D temperature profiles of statistically-steady simulations based on different tested algorithms. The lowest error per dataset is highlighted in bold.The plots in Fig. 2 corroborate the obtained statistics about the accuracy of different methods. When the simulation parameters we are predicting happen to be close to a simulation in the training set, the nearest neighbor profile already provides a good estimate. Otherwise, the NN predictions appear to be the most accurate. This is encouraging because it shows that even for small datasets such as the one we considered, machine learning can already start delivering useful results. Figure 2: Four temperature profiles from the cross-validation dataset (solid red lines) and the corresponding predictions from different models (dashed lines).OutlookWe are currently exploring how initial conditions impact the time a system takes to reach a statistically-steady state in terms of temperature and RMS velocity. We're comparing four starting points: hot, cold, linear temperature profiles, and predictions from our neural network. This will quantify the efficiency gain achieved by using the NN predictions to accelerate simulations towards equilibrium. In addition, we also plan to share the dataset and trained neural network with the community.

Introduction: Comet Interceptor [1] is a joint space mission of the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA). Its primary goals are to provide the first-ever in-situ characterisation of a long period comet (LPC), which could be a dynamically-new comet or an interstellar object, and to perform the first simultaneous multi-point exploration of a cometary coma and nucleus.Comet Interceptor is the first rapid response mission [2]. The mission will be launched in 2029 on Ariane 6 (together with the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) mission) towards the Sun-Earth Lagrange point L2, where it will wait for its target comet to appear.Comet Interceptor consists of one main spacecraft (S/C A), and two deployable probes, named Probe B1 and Probe B2, which are provided by JAXA and ESA, respectively, allowing multipoint investigations of the target. The payload covers remote sensing and in situ observations of the selected object during the fly-by.Science Objectives: The mission will investigate the processes of planetesimal formation by evaluating which of the phenomena observed by previous missions, particularly during the rendezvous of Rosetta with Comet 67P, are primordial and which have developed during the many perihelion passages of those SPCs. Specifically, the objectives of Comet Interceptor are:1) Comet Nucleus Science: What is the surface composition, shape, morphology, and structure of the target object?2) Comet Environment Science: What is the composition of the coma, its connection to the nucleus (activity) and the nature of its interaction with the solar wind?Science Activities: The search for the target comet is underway, and preparations are being made for the scientific exploitation of the data from the mission’s three spacecraft.The selection and scientific investigations of the target comet, as well as the development of the mission instruments and science operation areas, are supported by Working Groups (WGs). These are the Target Identification WG and Comet Environment WG. The latter comprises three sub-WGs, covering the Comet Nucleus, Near-Environment (inner dust and gas coma), and Far-Environment (outer dust and gas coma and tails). The specific tasks of the Comet Environment WG are to develop and provide results from scientific models addressing questions relevant to the implementation and operation of the mission and to achieve its scientific objectives.Here, we provide a brief overview of the mission, and present and describe the aims and activities of the working groups. One of the recent activities of all the Comet Environment sub-working groups is the preparation of a large repository of numerical models to favour the successful planning for scientific operation and investigation. Other activities include the analysis of available data to better understand the environment expected during the encounter and establish base line parameters for target selection. The potential comets to encounter are currently under evaluation, and preparations are being made for the scientific exploitation of the data from the mission’s three spacecraft.