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The only martian rock samples on Earth are meteorites ejected from the surface of Mars by asteroid impacts. The locations and geological contexts of the launch sites are currently unknown. Determining the impact locations is essential to unravel the relations between the evolution of the martian interior and its surface. Here we adapt a Crater Detection Algorithm that compile a database of 90 million impact craters, allowing to determine the potential launch position of these meteorites through the observation of secondary crater fields. We show that Tooting and 09-000015 craters, both located in the Tharsis volcanic province, are the most likely source of the depleted shergottites ejected 1.1 million year ago. This implies that a major thermal anomaly deeply rooted in the mantle under Tharsis was active over most of the geological history of the planet, and has sampled a depleted mantle, that has retained until recently geochemical signatures of Mars’ early history. The ejection sites of the martian meteorites are still unknown. Here, the authors build a database of 90 million craters and show that Tharsis region is the most likely source of depleted shergottites ejected 1.1 Ma ago, thus confirming that some portions of the mantle were recently anomalously hot.

The formation and differentiation of the crust of Mars in the first tens of millions of years after its accretion can only be deciphered from incredibly limited records. The martian breccia NWA 7034 and its paired stones is one of them. This meteorite contains the oldest martian igneous material ever dated: ~4.5 Ga old. However, its source and geological context have so far remained unknown. Here, we show that the meteorite was ejected 5–10 Ma ago from the north-east of the Terra Cimmeria—Sirenum province, in the southern hemisphere of Mars. More specifically, the breccia belongs to the ejecta deposits of the Khujirt crater formed 1.5 Ga ago, and it was ejected as a result of the formation of the Karratha crater 5–10 Ma ago. Our findings demonstrate that the Terra Cimmeria—Sirenum province is a relic of the differentiated primordial martian crust, formed shortly after the accretion of the planet, and that it constitutes a unique record of early crustal processes. This province is an ideal landing site for future missions aiming to unravel the first tens of millions of years of the history of Mars and, by extension, of all terrestrial planets, including the Earth. A new study pinpoints the ejection site of the 4.5-Ga-old Martian breccia NWA 7034 and paired stones to an area northeast of the Terra 679 Cimmeria–Sirenium province.

Impact craters are the most common feature on the Moon’s surface. Crater size–frequency distributions provide critical insight into the timing of geological events, surface erosion rates, and impact fluxes. The impact crater size–frequency follows a power law (meter‐sized craters are a few orders of magnitude more numerous than kilometric ones), making it tedious to manually measure all the craters within an area to the smallest sizes. We can bridge this gap by using a machine learning algorithm. We adapted a Crater Detection Algorithm to work on the highest resolution lunar image data set (Lunar Reconnaissance Orbiter‐Narrow‐Angle Camera [NAC] images). We describe the retraining and application of the detection model to preprocessed NAC images and discussed the accuracy of the resulting crater detections. We evaluated the model by assessing the results across six NAC images, each covering a different lunar area at differing lighting conditions. We present the model’s average true positive rate for small impact craters (down to 20 m in diameter) is 93%. The model does display a 15% overestimation in calculated crater diameters. The presented crater detection model shows acceptable performance on NAC images with incidence angles ranging between ∼50° and ∼70° and can be applied to many lunar sites independent to morphology. Plain Language Summary The Moon’s surface is covered in impact craters and recording their spatial density gives researchers the ability to study the geological evolution of our satellite. Analyzing craters helps in determining the physical properties of planetary surfaces and how/if impact rates change over time. These analyses rely on recording spatial densities for numerous surfaces, which has been achieved for craters >1–2 km on the Moon. Manually counting the smaller craters, which number in the hundreds of millions, is a daunting task. We adapted a Crater Detection Algorithm and applied it to the highest resolution lunar imagery data set. We describe our method for gathering, reformatting, and detecting craters across lunar images down to 20 m in diameter. The detection model performance was quantitatively evaluated across six different regions, each with different terrain and lighting conditions. Comparison between manually mapped craters and detections from our model allows us to conclude that the model has an acceptable performance in detecting fresh to moderately degraded craters of all sizes, down to 20 m in diameter, when compared to other studies. Automated crater detection complements manual counting methods and aids in unlocking secrets of the Moon’s surface. Key Points Adapted and retrained a Crater Detection Algorithm (using YOLOv3) to work on high‐resolution Lunar Reconnaissance Orbiter‐Narrow‐Angle Camera (NAC) images Developed a workflow for georeferencing and detecting craters down to 10 pixels in diameter across multiple overlapping NAC images Evaluation reveals acceptable performance in detecting craters on diverse terrains, across images with 50–70° incidence angles

Dating young lunar surfaces, such as impact ejecta blankets and terrains associated with recent volcanic activities, provides critical information on the recent events that shaped the surface of the Moon. Model age derivation of young or small areas using a crater chronology is typically achieved through manual counting, which requires a lot of small impact craters to be tediously mapped. In this study, we present the use of a Crater Detection Algorithm (CDA) to extract crater populations on Lunar Reconnaissance Orbiter—Narrow Angle Camera (LRO‐NAC) and Kaguya Terrain Camera images. We applied our algorithm to images covering the ejecta blankets of four Copernican impact craters and across four young mare terrains, where manually derived model ages were already published. Across the eight areas, 10 model ages were derived. We assessed the reproducibility of our model using two populations for each site: (a) an unprocessed population and (b) a population adjusted to remove contaminations of secondary and buried craters. The results showed that unprocessed detections led to overestimating crater densities by 12%–48%, but “adjusted” populations produced consistent results within <20% of published values in 80% of cases. Regarding the discrepancies observed, we found no significant error in our detections that could explain the differences with crater densities manually measured. With careful processing, we conclude that a CDA can be used to determine model ages and crater densities for the Moon. We also emphasize that automated crater datasets need to be processed, interpreted and used carefully, in unity with geologic reasoning. The presented approach can offer a consistent and reproducible way to derive model ages. Plain Language Summary Studying young lunar surfaces, such as impacted areas or volcanic activity, helps us understand recent events that have shaped the Moon's surface. Determining the model age of these areas generally involves manually counting small craters, which is time‐consuming and variable. This study presents a machine‐learning approach to detect craters on images acquired by the Lunar Reconnaissance Orbiter‐Narrow Angle Camera and the Kaguya Terrain Camera. Four impact craters and four young mare terrains were analyzed, where model ages had already been determined manually. When comparing our automatic counts to the manual counts, we observed that our results became more consistent with the published surface ages when we excluded secondary or buried craters from our crater populations. We also outline that automatic crater detection methods can be used to determine the age of lunar surfaces in a reliable and consistent manner when used correctly. Key Points Automatic crater counting of the Moon was achieved using a Convolutional Neural Network architecture and applied to LRO‐NAC and Kaguya Terrain Camera images Testing of the automatic counts against manual counts across the same count areas is required to provide confidence in the results Surface ages resulting from automatic crater counts are within acceptable error of model ages for the same area found using manual counts

Determining when an impact crater formed is a complex and tedious task. However, this knowledge is crucial to understanding the geological history of planetary bodies and, more specifically, gives information on erosion rate measurements, meteorite ejection location, impact flux evolution and the loss of a magnetic field. The derivation of an individual crater's age is currently performed through manual counting. Because crater size scales as a power law, this method is limited to small (and/or young) surface areas and, in the case of the derivation of crater emplacement age, to a small set of impact craters. Here, we used a Crater Detection Algorithm, specifically retrained to detect small impact craters on large‐ and high‐resolution imagery data set to solve this issue. We applied it to a global, 5 m/pixel resolution mosaic of Mars. Here, we test the use of this data set to date 10 large impact craters. We developed a cluster analysis tool in order to distinguish potential secondary crater clusters from the primary crater population. We then use this, filtered, crater population to date each large impact crater and evaluate our results against literature ages. We found that automated counting filtered through clustering analysis produced similar model ages to manual counts. This technique can now be expanded to much wider crater dating surveys, and by extension to any other kind of Martian surface. We anticipate that this new tool will considerably expand our knowledge of the geological events that have shaped the surface of Mars, their timing and duration. Plain Language Summary The age of an impact crater on a planetary surface is a crucial constraint in determining erosion rate, the crater source of Martian meteorites and the impact cratering flux evolution. This kind of measurement requires the counting of many impact craters superposed on the ejecta blanket of the considered crater and is therefore limited by human capability. To solve this issue, we adapted an automatic tool to detect small impact craters on the surface of Mars. We also developed an automatic approach to identify and remove clusters of small likely secondary craters detected by our algorithm. We assume these clusters of craters are formed by fragments ejected by an impact that formed a primary crater and need to be removed from crater densities used for age derivations. We applied our technique on 10 large Martian impact craters whose the formation age has been derived using manual counts and reported in the literature. We compared these ages to ours, derived from automatic count and automatic secondary craters filtering. Our results are consistent and indistinguishable from an age inferred from a manual count. For the first time, we demonstrate that an automated approach can deliver geologically meaningful model ages. Key Points Automatic detection of small craters on ejecta blanket of 10 large Martian craters Identification of secondary craters through cluster analysis Model ages from our semi‐automatic approach are similar from manual counts

Sedimentary volcanism, whereby material is brought to the surface by fluid overpressure, has been proposed to explain some of the periglacial landforms, including pitted cones, in the Northern Plains of Mars. However, in the absence of convincing mineralogical evidence, the origin for these deposits has never been conclusively determined. Here we conduct a remote sensing-based mineralogical survey to identify hydrated minerals within the Thumbprint Terrains and neighbouring Vastitas Borealis Formation. We detect several occurrences of hydrated silica along with sulfate salts in candidate mud volcano-like morphologies which likely formed during the Early Amazonian period, supporting the sedimentary volcanism origin. Buoyancy-driven analytical modelling suggests the hydrated silica and sulfate salts are sourced from reservoirs at depths of several 10 s and 100 s of metres, respectively below the Thumbprint Terrains and Vastitas Borealis Formation. The exposed sulfates may have been derived from ancient buried evaporite deposits suggesting, at least locally, a salt-rich aqueous origin for the Vastitas Borealis Formation, and would be consistent with the presence of a past northern ocean on Mars. Orbital observations identify the presence of hydrated silica and sulfate salts on Mars which is consistent with Early-Amazonian sedimentary volcanism and indicates the remobilization of aqueous reservoirs in the Northern Plains of the planet.

Impact craters on solar system bodies are used to determine the relative ages of surfaces. The smaller the limiting primary crater size, the higher the spatial resolution in surface/resurfacing age dating. A manually counted database (Robbins & Hynek, 2012, https://doi.org/10.1029/2011JE003966) of >384,000 craters on Mars >1 km in diameter exists. But because crater size scales as a power law, the number of impact craters in the size range 10 m to 1 km is in the tens of millions, a number making precise analysis of local variations of age, over an entire surface, impossible to perform by manual counting. To decode this crater size population at a planetary scale, we developed an automated Crater Detection Algorithm based on the You Only Look Once v3 object detection system. The algorithm was trained by annotating images of the controlled Thermal Emission Imaging System daytime infrared data set. This training data set contains 7,048 craters that the algorithm used as a learning benchmark. The results were validated against the manually counted database as the ground truth data set. We applied our algorithm to the Thermal Emission Imaging System global mosaic between ±65° of latitude, returning a true positive detection rate of 91% and a diameter estimation error (~15%) consistent with typical manual count variation. Importantly, although a number of automated crater counting algorithms have been published, for the first time we demonstrate that automatic counting can be routinely used to derive robust surface ages. Plain Language Summary Crater counting is the traditional method of determining the surface ages of planets throughout the solar system. This method, up to now, has used data that have been painstakingly counted by hand. The current published database for Mars contains hundreds of thousands of craters for diameters larger than 1 km. If we can count craters smaller than this, we will be able to target specific areas of interest to date. But the rate of impacts on planetary surfaces follows a power law such that the number of small (less than 1 km) craters is exponentially higher than the number of large craters. To count these requires an automated tool. Here we show that we have developed such a tool. We have validated the results against current manual databases. Importantly, and for the first time, we demonstrate that an automated crater counting tool can deliver geologically meaningful ages. Key Points We built a library of 7,048 craters to train a Crater Detection Algorithm (CDA) that we developed based on a neural network architecture We applied our algorithm to the Mars global THEMIS mosaic, generating results comparable to manual counting We applied the CDA to higher‐resolution images generating model ages indistinguishable (within error) from literature ages

The VESPA data access system focuses on applying Virtual Observatory (VO) standards and tools to Planetary Science. Building on a previous EC-funded Europlanet program, it has reached maturity during the first year of a new Europlanet 2020 program (started in 2015 for 4 years). The infrastructure has been upgraded to handle many fields of Solar System studies, with a focus both on users and data providers. This paper describes the broad lines of the current VESPA infrastructure as seen by a potential user, and provides examples of real use cases in several thematic areas. These use cases are also intended to identify hints for future developments and adaptations of VO tools to Planetary Science.

Joining dissimilar materials encounters significant engineering challenges due to the contrast in material properties that makes conventional welding not feasible. Magnetic Pulse Welding (MPW) offers a solid-state joining technique that overcomes these issues by using impact to create strong bonds without melting the substrate materials. This study investigates the weldability of aluminum alloy Al-5754 with Al-7075 and MARS 380 steel, used in armouring solutions of defense systems, by the use of MPW. In this work, weldability windows are investigated by varying standoff distances between the coating material and its substrate (0.25–4.5 mm) and discharge energies (5–13 kJ) with both O-shape and U-shape inductors. Mechanical strength of the welded joints were assessed through single lap shear tests, identifying optimal welding parameters. Then, the velocity profiles of the flyer plates were measured using heterodyne velocimetry to understand the dynamics of the impact. Then, substructures assembled with the optimal welding conditions were subjected to ballistic testing using 7.62 mm × 51 mm NATO and 9 mm × 19 mm Parabellum munitions to evaluate the resilience of the welds under ballistic impact. The outcomes demonstrate that MPW effectively joins Al-5754 with both Al-7075 and MARS 380, producing robust welds capable of withstanding ballistic impacts under certain conditions. This research advances the application of MPW in lightweight ballistic protection of defense systems, contributing to the development of more resilient and lighter protective structures.

Due to its outstanding mechanical resistance and resistance to corrosion, alloy 15-5 PH can be beneficially used for manufacturing aerospace structural parts. Following exposure to intermediate temperature, from 300°–400 °C, the alloy embrittles through the decomposition of the martensite into iron-rich and chromium-rich domains. Depending on the ageing time, these domains are either interconnected or unconnected with each other. The embrittlement results in a drastic drop of the impact strength-energy and an increase of the ductile-to-brittle transition temperature. The initial microstructure and mechanical properties can be recovered through a re-homogenization of the distribution of chromium and iron atoms in the material in the case where the decomposition of the matrix is not too pronounced. The application of a stress higher than 60 per cent of the yield strength further enhances the ageing kinetics in the case where the combined effect of temperature and time results in the spinodal decomposition of the martensite.