To address questions about the multiple lunar nearside–farside dichotomies and to provide new insights into both the early impact history of the Solar System and the geological evolution of the Moon, the Chang’e-6 (CE-6) landing zone has been selected to lie within the lunar farside South Pole–Aitken (SPA) basin in the southern part of the Apollo basin (150–158° W, 41–45° S), a site that provides access to a diversity of SPA material. Here, we describe the geomorphology, geology and chronology of three candidate sampling sites within this zone that are likely to ensure safe landing and sampling. The geological characteristics indicate that CE-6 is expected to collect lunar farside SPA ejecta fragments, possible mantle material and young (roughly 2.40 Gyr-year-old) and/or old (roughly 3.43 Gyr-year-old) basaltic material, all of which will provide important guidance for future in situ farside sample collection and deepen our understanding of the evolution of the Moon.

Chang’E-4 (CE-4) was the first mission to accomplish the goal of a successful soft landing on the lunar farside. The landing trajectory and the location of the landing site can be effectively reconstructed and determined using series of images obtained during descent when there were no Earth-based radio tracking and the telemetry data. Here we reconstructed the powered descent trajectory of CE-4 using photogrammetrically processed images of the CE-4 landing camera, navigation camera, and terrain data of Chang’E-2. We confirmed that the precise location of the landing site is 177.5991°E, 45.4446°S with an elevation of −5935 m. The landing location was accurately identified with lunar imagery and terrain data with spatial resolutions of 7 m/p, 5 m/p, 1 m/p, 10 cm/p and 5 cm/p. These results will provide geodetic data for the study of lunar control points, high-precision lunar mapping, and subsequent lunar exploration, such as by the Yutu-2 rover.

The distribution range, time-varying characteristics, and sources of lunar water are still controversial. Here we show the Chang’E-5 in-situ spectral observations of lunar water under Earth’s magnetosphere shielding and relatively high temperatures. Our results show the hydroxyl contents of lunar soils in Chang’E-5 landing site are with a mean value of 28.5 ppm, which is on the weak end of lunar hydration features. This is consistent with the predictions from remote sensing and ground-based telescopic data. Laboratory analysis of the Chang’E-5 returned samples also provide critical clues to the possible sources of these hydroxyl contents. Much less agglutinate glass contents suggest a weak contribution of solar wind implantation. Besides, the apatite present in the samples can provide hydroxyl contents in the range of 0 to 179 ± 13 ppm, which shows compelling evidence that, the hydroxyl-containing apatite may be an important source for the excess hydroxyl observed at this young mare region.

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.