Li Shao

The Chinese Space-station Survey Telescope (CSST) is a flagship mission designed to carry out a large-area high-resolution multiband imaging and slitless spectroscopic survey over a decade. The survey's science drivers, ranging from precision cosmology to stellar astrophysics, impose stringent requirements on the performance of its focal plane detectors. This paper is the first in a series that present ground-based characterization of the 31 flight-model charge-coupled devices (CCDs) of the CSST Survey Camera. Measurements of basic electro-optical performance are reported here. Key results include: full-well capacity ≧ 80 ke ^- (limited by the test electronics), non-linearity < 1.4% in the range of 7-70 ke ^- , median readout noise ∼ 3.6 e ^- , dark current ≦ 0.02 e ^- pix ^-1 s ^-1 at the operating temperature of -85 degree celsius, high quantum efficiency across the survey's spectral range (255-1000 nm), typical pixel response non-uniformity < 1%, and charge transfer efficiency > 99.9994% at the flat-field level of 10 ke ^- . All 31 CCDs meet or exceed the Survey Camera's specifications. These measurements establish a performance baseline and provide inputs for CSST image simulation and data processing pipelines.

The Chinese Space-station Survey Telescope (CSST) will perform a decade-long high-precision wide-field imaging survey that relies on rigorous on-orbit calibration. This necessitates stable celestial benchmark fields to maintain photometric and astrometric consistency throughout the mission lifetime. We establish comprehensive selection criteria including observational visibility, stellar number density, bright-star contamination, interstellar dust extinction, and color–magnitude coverage. Using the CSST Observational Strategy Analysis Tool ( COSAT ) and all-sky dust maps from Planck and Schlegel, Finkbeiner & Davis, we constrain eligible regions to the ranges of ecliptic latitude ∣ β ∣ ≥ 50° and Galactic latitude ∣ b ∣ ≥ 15°. From an initial sample of 29 candidate clusters meeting these spatial constraints, six globular clusters (M13, M92, NGC 104, NGC 362, NGC 1261, and NGC 1851) are identified as optimal calibration fields, fulfilling all the critical criteria. These selected clusters are recommended as optimal calibration field candidates for CSST’s on-orbit calibration program, and are fundamental to achieve unprecedented photometric precision in CSST’s space-based survey.

Improving the accuracy of photometric redshifts (photo- z ) is essential for reliable statistical studies of cosmology and galaxy evolution. However, missing photometric bands are a common observational challenge that can significantly degrade photo- z estimation accuracy. In this work, we present a systematic evaluation of data imputation methods aimed at improving photo- z performance. We benchmark a range of representative machine learning and deep learning architectures, identifying k-nearest neighbors (KNN) and the attentionbased SAITS model as the leading performers. These models are then applied to China Space Station Survey Telescope mock data to assess their performance under realistic observational conditions. Our results show that KNN yields the highest accuracy under idealized missing completely at random (MCAR) conditions with complete training sets, whereas robustness tests reveal that SAITS significantly outperforms KNN when training data are incomplete or when applied to realistic mixed-mechanism scenarios. We find that domain consistency between training and testing missingness patterns is a prerequisite for optimal performance, highlighting the risks of domain shift in supervised regression tasks. Furthermore, our analysis demonstrates that while general imputation models are highly effective for MCAR and missing at random data, they are detrimental when applied to missing not at random data arising from flux limits, as statistical models fail to capture the physical information inherent in these nondetection. Consequently, we advocate for more sophisticated architectures capable of disentangling stochastic missingness from physical nondetection to address these distinct mechanisms individually.

The Multi-Channel Imager (MCI) is a powerful near-ultraviolet (NUV) and visible imager onboard the Chinese Space Station Survey Telescope (CSST). The MCI provides three imaging channels, which are the NUV channel, the Blue channel and the Red channel, with the wavelength ranges of 255–430 nm, 430–700 nm, and 700–1000 nm, respectively. MCI’s three channels can target the same field simultaneously, which is unique compared to other imagers onboard the Hubble Space Telescope (HST) or the James Webb Space Telescope (JWST). Each channel employs a CCD focal plane of 9216 × 9232 pixels and ∼7 . ′ 5 × 7 . ′ 5 field of view (FOV), which are ≳4 times the FOVs of HST imagers. The MCI’s three channels feature unprecedented sensitivities and FOVs, complementing the NUV and visible capabilities of the CSST for high-precision photometry and weak-signal detection, which would help build a new standard-star system and the deepest UV-Optical exposures for CSST. Rich filter sets of MCI would help explore other areas of science such as local emission line mapping, searching for high- z Ly α emitters, etc. Here we present key design features, results of current ground tests, and suggest observing strategies for the MCI.

The Chinese Space Station Survey Telescope (CSST) is a flagship space-based observatory. Its main survey camera is designed to conduct high spatial resolution near-ultraviolet to near-infrared imaging and low-resolution spectroscopic surveys. To maximize the scientific output of CSST, we have developed a comprehensive, high-fidelity simulation pipeline for reproducing both imaging and spectroscopic observations. This paper presents an overview of the simulation framework, detailing its implementation and components. Built upon the GalSim package and incorporating the latest CSST instrumental specifications, our pipeline generates pixel-level mock observations that closely replicate the expected instrumental and observational conditions. The simulation suite integrates realistic astrophysical object catalogs, instrumental effects, point-spread function modeling, and observational noises to produce accurate synthetic data. We describe the key processing stages of the simulation, from constructing the input object catalogs to modeling the telescope optics and detector responses. Furthermore, we introduce the most recent release of simulated datasets, which provide a crucial testbed for data processing pipeline developments, calibration strategies, and scientific analyses, ensuring that CSST will meet its stringent requirements. Our pipeline serves as a vital tool for optimizing CSST main survey strategies and ensuring robust cosmological measurements.

Many of the tensions in cosmological models of the Universe lie in the low-mass, low-velocity regime. Probing this regime requires a statistically significant sample of galaxies with well-measured kinematics and robustly measured uncertainties. The Widefield ASKAP L -band Legacy All-sky Blind surveY (WALLABY), as a wide-area, untargeted H i survey, is well positioned to construct this sample. As a first step toward this goal, we develop a framework for testing kinematic modeling codes in the low-resolution, low signal-to-noise ratio, low rotation velocity regime. We find that the WALLABY Kinematic Analysis Proto-Pipeline is remarkably successful at modeling these galaxies when compared to other algorithms, but, even in idealized tests, there is a significant fraction of false positives found below inclinations of ≈40°. We further examine the 11 detections with rotation velocities below 50 km s ⁻¹ in the WALLABY pilot data releases. We find that those galaxies with inclinations above 40° lie within 1 σ –2 σ of structural scaling relations that require reliable rotation velocity measurements, such as the baryonic Tully–Fisher relation. Moreover, the subset that has consistent kinematic and photometric inclinations tends to lie nearer to the relations than those that have inconsistent inclination measures. This work both demonstrates the challenges faced in low-velocity kinematic modeling and provides a framework for testing modeling codes as well as constructing a large sample of well-measured low-rotation models from untargeted surveys.

As part of the pilot survey of the Widefield ASKAP L-band Legacy All-sky Survey (WALLABY), high-resolution neutral atomic hydrogen (HI) observations of the dwarf galaxy pair NGC 4532/DDO 137 (WALLABY J123424+062511) have revealed a huge (48 kpc) bridge of gas between the two galaxies, as well as numerous arms and clouds that connect with the even longer (0.5 Mpc) tail of gas previously discovered with the Arecibo telescope. Our modelling suggests that a combination of ram pressure and tidal forces is responsible for the nature of the system. Although the pair lies well outside of the virial radius of the Virgo cluster, ram pressure due to infall through an extensive envelope of hot gas around the cluster is most likely responsible for the HI tail. Over a time-scale of 1 Gyr, the predicted electron density ($1.2\times 10^{-5}$ cm$^{-3}$) and infall velocity (880 km s$^{-1}$) are probably sufficient to explain the extensive stripping from the common gaseous envelope of NGC 4532/DDO 137. The ongoing tidal interaction with the Virgo cluster appears to have prevented a rapid merger of the binary pair, with the mutual tidal interaction between the galaxy pair being responsible for raising gas from the outer parts of the galaxy potential wells into the HI bridge and common envelope. The NGC 4532/DDO 137 system mirrors many of the physical features of the Magellanic System, and may lead to a better understanding of that system, as well as casting more light on the relative importance of interaction mechanisms in the outskirts of dynamically young galaxy clusters such as Virgo.

Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong-lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine-learning algorithms and applied to cutout-centered galaxies. However, according to the design and survey strategy of optical surveys by the China Space Station Telescope (CSST), preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual transformer with a sliding window technique to search for strong-lensing systems within entire images. Moreover, given that multicolor images of strong-lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong-lensing systems in images with any number of channels. As evaluated using CSST mock data based on a semianalytic model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. A total of 61 new strong-lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.