Chang'e‐5 successfully returned ∼1,731 g of lunar samples on December 17, 2020. We systematically studied the morphology and morphometry of craters surrounding the Chang'e‐5 site based on high‐resolution images. A key ejecta source crater database was produced, incorporating their morphometrical parameters, excavation depth, target rock types, and ages from either crater counting, crater morphology or degradation methods. We found that (a) the regolith at the Chang'e‐5 site is ∼4–6 m thick, (b) ejecta are primarily from five proximal craters, including Xu Guangqi, dominantly with ages <200 Ma; (c) distal materials are mainly from Harpalus, Copernicus, Aristarchus, and Mairan G craters; (d) the drill samples are likely to be mainly from Impact Crater (IC)‐261 and IC‐265 particles at the top and the contribution of Xu Guangqi increases with depth; (e) the ejecta of Xu Guangqi would buried preexisting materials but probably overturned by postimpacts.

Mapping planetary surfaces is an intricate task that forms the basis for many geologic, geomorphologic, and geographic studies of planetary bodies. In this work, we present a method to automate a specific type of planetary mapping, geomorphic mapping, taking machine learning as a basis. Additionally, we introduce a novel dataset, termed DoMars16k, which contains 16,150 samples of fifteen different landforms commonly found on the Martian surface. We use a convolutional neural network to establish a relation between Mars Reconnaissance Orbiter Context Camera images and the landforms of the dataset. Afterwards, we employ a sliding-window approach in conjunction with a Markov Random field smoothing to create maps in a weakly supervised fashion. Finally, we provide encouraging results and carry out automated geomorphological analyses of Jezero crater, the Mars2020 landing site, and Oxia Planum, the prospective ExoMars landing site.

Ring‐moat dome structures (RMDSs) are small circular mounds of diameter typically about 200 m and ∼3–4 m in height, surrounded by narrow, shallow moats. They occur in clusters, are widespread in ancient Imbrian‐aged mare basalt host units and show mineralogies comparable to those of their host units. Based on these close associations and similarities, a model has been proposed for the formation of RMDS as the result of late‐stage flow inflation, with second boiling releasing quantities of magmatic volatiles that migrate to the top of the flow as magmatic foams and extrude through cracks in the cooled upper part of the flow to produce the small RMDS domes and surrounding moats. In contrast to this model advocating a contemporaneous emplacement of RMDSs and their host lava flows, a range of observations suggests that the RMDS formed significantly after the emplacement and cooling of their host lava flows, perhaps as recently as in the Copernican Period (∼1.1 Ga to the present). These observations include: (a) stratigraphic embayment of domes into post‐lava flow emplacement impact craters; (b) young crater degradation age estimates for the underlying embayed craters; (c) regolith development models that predict thicknesses in excess of the observed topography of domes and moats; (d) landform diffusional degradation models that predict very young ages for mounds and moats; (e) suggestions of fewer superposed craters on the mounds than on the adjacent host lava flows, and (f) observations of superposed craters that suggest that the mound substrate does not have the properties predicted by the magmatic foam model. Together, these observations are consistent with the RMDS formation occurring during the period after the extrusion and solidification of the host lava flows, up to and including the geologically recent Late Copernican, that is, the last few hundreds of millions of years of lunar history. We present and discuss each of these contradictory data and interpretations and summarize the requirements for magma ascent and eruption models that might account for young RMDS ages. We conclude with a discussion of the tests and future research and exploration that might help resolve the RMDS age and mode of emplacement conundrum.

Holocaust Narratives: Trauma, Memory and Identity Across Generations analyzes individual multi-generational frameworks of Holocaust trauma to answer one essential question: How do these narratives change to not only transmit the trauma of the Holocaust – and in the process add meaning to what is inherently an event that annihilates meaning – but also construct the trauma as a connector to a past that needs to be continued in the present? Meaningless or not, unspeakable or not, unknowable or not, the trauma, in all its impossibilities and intractabilities, spawns literary and scholarly engagement on a large scale. Narrative is the key connector that structures trauma for both individual and collective.

On the Moon, in the near infrared wavelength range, spectral diagnostic features such as the 1-$\mathsf{\mu }$m and 2-$\mathsf{\mu }$m absorption bands can be used to estimate abundances of the constituent minerals. However, there are several factors that can darken the overall spectrum and dampen the absorption bands. Namely, (1) space weathering, (2) grain size, (3) porosity, and (4) mineral darkening agents such as ilmenite have similar effects on the measured spectrum. This makes spectral unmixing on the Moon a particularly challenging task. Here, we try to model the influence of space weathering and mineral darkening agents and infer the uncertainties introduced by these factors using a Markov Chain Monte Carlo method. Laboratory and synthetic mixtures can successfully be characterized by this approach. We find that the abundance of ilmenite, plagioclase, clino-pyroxenes and olivine cannot be inferred accurately without additional knowledge for very mature spectra. The Bayesian approach to spectral unmixing enables us to include prior knowledge in the problem without imposing hard constraints. Other data sources, such as gamma-ray spectroscopy, can contribute valuable information about the elemental abundances. We here find that setting a prior on TiO₂ and Al₂O₃ can mitigate many of the uncertainties, but large uncertainties still remain for dark mature lunar spectra. This illustrates that spectral unmixing on the Moon is an ill posed problem and that probabilistic methods are important tools that provide information about the uncertainties, that, in turn, help to interpret the results and their reliability.

The instruments of the Mars Reconnaissance Orbiter (MRO) provide a large quantity and variety of imagining data for investigations of the Martian surface. Among others, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures visible to infrared reflectance across several hundred spectral bands. However, Mars is only partially covered with targeted CRISM at full spectral and spatial resolution. In fact, less than one percent of the Martian surface is imaged in this way. In contrast, the Context Camera (CTX) onboard the MRO delivers images with a higher spatial resolution and the image data cover almost the entire Martian surface. In this work, we examine to what extent machine learning systems can learn the relation between morphology, albedo and spectral composition. To this end, a dataset of 67 CRISM-CTX image pairs is created and different deep neural networks are trained for the pixel-wise prediction of CRISM bands solely based on the albedo information of a CTX image. The trained models enable us to estimate spectral bands across large areas without existing CRISM data and to predict the spectral composition of any CTX image. The predictions are qualitatively similar to the ground-truth spectra and are also able to recover finer grained details, such as dunes or small craters.