Earth and Planetary Astrophysics (astro-ph.EP)

Abstract: We report the discovery of HN Lib b, a sub-Neptunian mass planet orbiting the nearby ($d \approx$ = 6.25 pc) M4.0 V star HN Lib detected by our CARMENES radial-velocity (RV) survey. We determined a planetary minimum mass of $M_\text{b}\sin i = $ 5.46 $\pm$ 0.75 $\text{M}_\oplus$ and an orbital period of $P_\text{b} = $ 36.116 $\pm$ 0.029 d, using $\sim$5 yr of CARMENES data, as well as archival RVs from HARPS and HIRES spanning more than 13 years. The flux received by the planet equals half the instellation on Earth, which places it in the middle of the conservative habitable zone (HZ) of its host star. The RV data show evidence for another planet candidate with $M_\text{[c]}\sin i = $ 9.7 $\pm$ 1.9 $\text{M}_\oplus$ and $P_\text{[c]} = $ 113.46 $\pm$ 0.20 d. The long-term stability of the signal and the fact that the best model for our data is a two-planet model with an independent activity component stand as strong arguments for establishing a planetary origin. However, we cannot rule out stellar activity due to its proximity to the rotation period of HN Lib, which we measured using CARMENES activity indicators and photometric data from a ground-based multi-site campaign as well as archival data. The discovery adds HN Lib b to the shortlist of super-Earth planets in the habitable zone of M dwarfs, but HN Lib [c] probably cannot be inhabited because, if confirmed, it would most likely be an icy giant.

Abstract: Recent advances in modeling density distributions, so-called neural density fields, can accurately describe the density distribution of celestial bodies without, e.g., requiring a shape model - properties of great advantage when designing trajectories close to these bodies. Previous work introduced this approach, but several open questions remained. This work investigates neural density fields and their relative errors in the context of robustness to external factors like noise or constraints during training, like the maximal available gravity signal strength due to a certain distance exemplified for 433 Eros and 67P/Churyumov-Gerasimenko. It is found that both models trained on a polyhedral and mascon ground truth perform similarly, indicating that the ground truth is not the accuracy bottleneck. The impact of solar radiation pressure on a typical probe affects training neglectable, with the relative error being of the same magnitude as without noise. However, limiting the precision of measurement data by applying Gaussian noise hurts the obtainable precision. Further, pretraining is shown as practical in order to speed up network training. Hence, this work demonstrates that training neural networks for the gravity inversion problem is appropriate as long as the gravity signal is distinguishable from noise. Code and results are available at https://github.com/gomezzz/geodesyNets

Abstract: The search for ephemeral liquid water on Mars is an ongoing activity. After the recession of the seasonal polar ice cap on Mars, small water ice patches may be left behind in shady places due to the low thermal conductivity of the Martian surface and atmosphere. During late spring and early summer, these patches may be exposed to direct sunlight and warm up rapidly enough for the liquid phase to emerge. To see the spatial and temporal occurrence of such ice patches, optical images should be searched for and checked. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the High Resolution Imaging Science Experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter space mission. Out of these, 37 images were identified with smaller ice patches, which were distinguishable by their brightness, colour and strong connection to local topographic shading. In this study, a convolutional neural network (CNN) is applied to find further images with potential water ice patches in the latitude band between -40{\deg} and -60{\deg}, where the seasonal retreat of the polar ice cap happens. Previously analysed HiRISE images are used to train the model, each was split into hundreds of pieces, expanding the training dataset to 6240 images. A test run conducted on 38 new HiRISE images indicates that the program can generally recognise small bright patches, however further training might be needed for more precise predictions.Using a CNN model may make it realistic to analyse all available surface images, aiding us in selecting areas for further investigation.

Abstract: A mathematical model and software implementation developed to predict trajectories of single lunar dust particles acted on by a high velocity gas flow is discussed. The model uses output from a computation fluid dynamics (CFD) or direct simulation Monte Carlo (DSMC) simulation of a rocket nozzle hot gas jet. The gas density, velocity vector field, and temperature predicted by the CFD/DSMC simulations, provide the data necessary to compute the forces and accelerations acting on a single particle of regolith. All calculations of trajectory assume that the duration of particle flight is much shorter than the change in gas properties, i.e., the particle trajectory calculations take into account the spatial variation of the gas jet, but not the temporal variation. This is a reasonable first-order assumption. Final results are compared to photogrammetry derived estimates of dust angles form Apollo landing videos.

Abstract: The physical state of the lunar soil in the permanently shadowed craters of the moon is inferred from experimental investigation. The permanently shadowed craters do not undergo the same thermal cycling experienced by other parts of the moon and therefore could be slightly less compacted. This study is significant because excavating, roving, and landing interactions, along with the energy budgets and deployment schedules for associated technology, need to be scaled and designed properly. Results indicate that the degree of compaction due to thermal cycling is a function of the depth in the soil column.