We present the Star cluster Hunting Pipeline (SHiP) that can identify star clusters in Gaia second data release (DR2) data and establish a star cluster catalog for the Galactic disk. A friend-of-friend-based cluster finder method is used to identify star clusters using five-dimensional stellar parameters, , and μ _δ . Our new catalog contains 2443 star cluster candidates identified from disk stars located within and with G < 18 mag. An automatic isochrone fitting scheme is applied to all cluster candidates. With a combination of parameters obtained from isochrone fitting, we classify cluster candidates into three classes (Class 1, 2, and 3). Class 1 clusters are the most probable star cluster candidates with the most stringent criteria. Most of these clusters are nearby (within 4 kpc). Our catalog is crossmatched with three Galactic star cluster catalogs, Kharchenko et al., Gaudin et al., and Bica et al. The proper motion and parallax of matched star clusters are in good agreement with these earlier catalogs. We discover 76 new star cluster candidates that are not listed in these three catalogs. The majority of these are clusters older than log(age/yr) = 8.0 and are located in the inner disk with . The recent discovery of new star clusters suggests that current Galactic star cluster catalogs are still incomplete. Among the Class 1 cluster candidates, we find 56 candidates for star cluster groups. The pipeline, the catalog, and the member list containing all candidates star clusters and star cluster groups have been made publicly available.

We compare the stellar masses of central and satellite galaxies predicted by three independent semi-analytical models (SAMs) with observational results obtained from a large galaxy group catalog constructed from the Sloan Digital Sky Survey. In particular, we compare the stellar mass functions of centrals and satellites, the relation between total stellar mass and halo mass, and the conditional stellar mass functions, Φ(M _*|M _h), which specify the average number of galaxies of stellar mass M _* that reside in a halo of mass M _h. The SAMs only predict the correct stellar masses of central galaxies within a limited mass range and all models fail to reproduce the sharp decline of stellar mass with decreasing halo mass observed at the low mass end. In addition, all models over-predict the number of satellite galaxies by roughly a factor of 2. The predicted stellar mass in satellite galaxies can be made to match the data by assuming that a significant fraction of satellite galaxies are tidally stripped and disrupted, giving rise to a population of intra-cluster stars (ICS) in their host halos. However, the amount of ICS thus predicted is too large compared to observation. This suggests that current galaxy formation models still have serious problems in modeling star formation in low-mass halos.

Impact craters are the most significant topographic features on the lunar surface. They are also essential factors affecting the construction of lunar bases and activities on the lunar surface in the future. Nevertheless, it is difficult to extract the impact craters by image processing methods, due to the influence of illumination in the lunar polar region. Furthermore, the digital elevation model (DEM) is unable to extract small impact craters. Therefore, we propose an extraction framework for small craters, namely AE-TransUNet+, for the future lunar South Pole region of interest. The algorithm is applied to grayscale images (0.5 m/pixel) captured by the Lunar Reconnaissance Orbital (LRO) Narrow-Angle Camera (NAC). To improve the channel extract and spatial relationship between features, the Convolutional Block Attention Modules (CBAM) and Depthwise-separable Convolution (DSC) are added to the core TransUNet model, and the redesigned jump connection enhancement module focuses more on small impact crater features. Experiments on the NAC dataset show that AE-TransUNet+ has a higher F1-Score (0.743) and IOU (0.847) compared to other crater detection models. Besides, based on transfer learning, the visualization results of Mars and Mercury shows that the proposed method is more efficient in the processing of remote sensing data for different deep space explorations providing better mobility.

We test Verlinde’s Emergent Gravity (EG) theory using a galaxy–galaxy lensing technique based on Sloan Digital Sky Survey DR7 data. In the EG scenario, we do not expect a color dependence of the galaxy sample in the apparent dark matter predicted by EG, which is exerted only by the baryonic mass. If the baryonic mass is similar, then the predicted lensing profiles from the baryonic mass should be similar according to EG, regardless of the color of the galaxy sample. We use the stellar mass of the galaxy as a proxy of its baryonic mass. We divide our galaxy sample into five stellar mass bins, and further classify them as red and blue subsamples in each stellar mass bin. If we set the halo mass and concentration as free parameters, ΛCDM is favored by our data in terms of the reduced χ ², while EG fails to explain the color dependence of the excess surface density from the galaxy–galaxy lensing measurement.

We present OmniUV, a multipurpose simulation toolkit for space and ground VLBI observations. It supports various kinds of VLBI stations, including Earth (ground) fixed, Earth orbit, Lunar fixed, Lunar orbit, Moon–Earth, and Earth–Sun Lagrange 1 and 2 points, etc. The main functionalities of this toolkit are: (1) trajectory calculation; (2) baseline uv calculation, by taking the availability of each station into account; (3) visibility simulation for the given uv distribution, source structure, and system noise; (4) image and beam reconstruction. Two scenarios, namely the space VLBI network and the wide-field array, are presented as the application of the toolkit at completely different scales. OmniUV is the acronym of “Omnipotent UV.” We hope it could work as a general framework, in which various kinds of stations could be easily incorporated and the functionalities could be further extended. The toolkit has been made publicly available.