arXiv subjects

Zhang Ban

Publications and source records attributed to Zhang Ban.

9 recordsLinked to original sources

arXiv2026

Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance

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 (ML) and deep learning (DL) architectures, identifying k-nearest neighbors (KNN) and the attention-based SAITS model as the leading performers. These models are then applied to China Space Station Survey Telescope (CSST) 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 is 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 (MAR) data, they are detrimental when applied to missing not at random (MNAR) data arising from flux limits, as statistical models fail to capture the physical information inherent in these non-detections. Consequently, we advocate for more sophisticated architectures capable of disentangling stochastic missingness from physical non-detections to address these distinct mechanisms individually.

astro-ph.GA↗

arXiv2026

A Robust Geometric Distortion Solution for Main Survey Camera of CSST

The advancement in sensitivity and field of view of next-generation wide-field survey telescopes requires astrometric measurements with high precision, even in the presence of significant geometric distortions. To address this challenge, we develop a Weighted Polynomial Distortion Correction in 2-Phase (WPDC-2P) method. This approach enhances stellar cross-matching, incorporates distance-based weighting into the traditional polynomial fitting, and employs a look-up table to absorb the remaining distortion residuals. Validated on simulated data from the Main Survey Camera of the \emph{Chinese Space Station Survey Telescope} (CSST), incorporating geometric distortions up to approximately $200$ pixels, the method achieves astrometric standard deviation ranging from 0.013 to 0.107 pixels (0.03 pixels for the $g$-1 detector) across all 18 detectors. Under extreme crowding conditions (e.g., globular cluster NGC 2298), the astrometric precision for the $g$-1 detector reaches 0.05-pixel level within the central region ($r_d < 4000$), despite a centroiding precision of $\sim$0.04 pixels. When applied to the Beijing-Arizona Sky Survey data, for which the standard pipeline delivers an astrometric uncertainty of $\sim$20 mas, our method reduces the positional scatter to $ \sigma_{\Delta\alpha}=5.494$ mas (0.01 pixels) and $ \sigma_{\Delta\delta}=9.981$ mas (0.02 pixels) using only a weighted 3rd-order polynomial correction. The method has been integrated into the CSST data processing pipeline and is prepared for further refinement using on-orbit calibration data.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: Multi-Channel Imager--Instrument Simulation

The Chinese Space Station Survey Telescope (CSST), a two-meter aperture astronomical space telescope under China's manned space program, is equipped with multiple back-end scientific instruments. As an astronomical precision measurement module of the CSST, the Multi-Channel Imager (MCI) can cover a wide wavelength range from ultraviolet to near-infrared with three-color simultaneous high-precision photometry and imaging, which meets the scientific requirements for various fields. The diverse scientific objectives of MCI require not only a robust airborne platform, advanced optical systems, and observing facilities but also comprehensive software support for scientific operations and research. To this end, it is essential to develop realistic observational simulation software to thoroughly evaluate the MCI data stream and provide calibration tools for future scientific investigations. The MCI instrument simulation software will serve as a foundation for the development of the MCI data processing pipeline and will facilitate improvements in both hardware and software, as well as in the observational operation strategy, in alignment with the mission's scientific goals. In conclusion, we present a comprehensive overview of the MCI instrument simulation and some corresponding performances of the MCI data processing pipeline.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: Integral Field Spectrograph--Instrument Simulation

The Chinese Space Station Survey Telescope (CSST) is a next-generation Stage-IV facility renowned for its wide field of view, high image quality, and multi-band observational capabilities. Among the five instruments onboard the CSST, the Integral Field Spectrograph (IFS) offers the unique ability to simultaneously capture spatial and spectral information across a field of view of no less than $6^{''}\times6^{''}$. Key advantages of the IFS include a high spatial resolution of $0.2^{''}$ and a broad spectral coverage from 350 to 1000 nm, making it an ideal instrument for studying physical processes in the vicinity of supermassive black holes within galaxies. To more accurately assess the technical and scientific performance of the CSST-IFS, it is essential to develop a simulation tool that incorporates realistic effects from all optical components. Such a simulation will form an integral part of the CSST-IFS data and pipeline system, enabling the development of the data reduction pipeline well ahead of actual observations. This paper presents an end-to-end simulation workflow for the CSST-IFS, incorporating a wide range of instrumental effects that may influence its spectral and imaging performance. The simulation accounts for optical diffraction effects introduced by all components, such as image slicers and slit array, as well as sub-pixel effects from gratings. It also includes various detector noises, frame-shifting effects, and charge-transfer inefficiency. Real observational conditions--such as target Doppler shift, cosmic rays, and other in-orbit operational effects--are also considered. We describe the technical implementation of the simulation and present results that quantitatively characterize key instrument parameters.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: End-to-End Performance Modeling of Optical System

This study presents a comprehensive end-to-end simulation analysis of the optical imaging performance of the China Survey Space Telescope (CSST) under in-orbit conditions. An integrated system model incorporating five static and two dynamic error sub-models was established. Wavefront errors were calculated for each sub-model and compared to the integrated system error to quantify the individual contributions to image degradation. At the detector level, wavefront error, point spread function (PSF), and ellipticity were evaluated across the full field of view (FOV). The average radius of 80\% encircled energy (REE80) of the PSF under full-error conditions was determined for 25 field points, yielding a value of 0.114 arcseconds. Furthermore, the calculations indicate a correlation between the wavefront distribution and the ellipticity distribution within the optical system. By optimizing the wavefront distribution, it is possible to adjust the ellipticity distribution of the PSF across the full FOV. The end-to-end simulation approach adopted in this paper provides a theoretical foundation for improving the image quality in large-aperture, off-axis space telescopes.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: Main Surveys--An Overview of Framework and Simulation Suite

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 (PSF) 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.

astro-ph.IM↗

arXiv2024

CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)

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 cut-out-centered galaxies. However, according to the design and survey strategy of optical surveys by 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 multi-color 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 an Semi-Analytic 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. 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.

astro-ph.IM↗

arXiv2023

Forklens: Accurate weak-lensing shear measurement with deep learning

Weak gravitational lensing is one of the most important probes of the nature of dark matter and dark energy. In order to extract cosmological information from next-generation weak lensing surveys (e.g., Euclid, Roman, LSST, and CSST) as much as possible, accurate measurements of weak lensing shear are required. There are existing algorithms to measure the weak lensing shear on imaging data, which have been successfully applied in previous surveys. In the meantime, machine learning (ML) has been widely recognized in various astrophysics applications in modeling and observations. In this work, we present a fully deep-learning-based approach to measuring weak lensing shear accurately. Our approach comprises two modules. The first one contains a convolutional neural network (CNN) with two branches for taking galaxy images and point spread function (PSF) simultaneously, and the output of this module includes the galaxy's magnitude, size, and shape. The second module includes a multiple-layer neural network (NN) to calibrate weak-lensing shear measurements. We name the program Forklens and make it publicly available online. Applying Forklens to CSST-like mock images, we achieve consistent accuracy with traditional approaches (such as moment-based measurement and forward model fitting) on the sources with high signal-to-noise ratios (S/N, > 20). For the sources with S/N < 10, Forklens exhibits an $\sim 36\%$ higher Pearson coefficient on galaxy ellipticity measurements. After adopting galaxy weighting, the shear measurements with Forklens deliver accuracy levels to $0.2\%$. The whole procedure of Forklens is automated and costs about $0.7$ milliseconds per galaxy, which is appropriate for adequately taking advantage of the sky coverage and depth of the upcoming weak lensing surveys.

astro-ph.CO↗

arXiv2022

Tolerance For the Pixelation Effect in Shear Measurement

Images taken by space telescopes typically have a superb spatial resolution, but a relatively poor sampling rate due to the finite CCD pixel size. Beyond the Nyquist limit, it becomes uncertain how much the pixelation effect may affect the accuracy of galaxy shape measurement. It is timely to study this issue given that a number of space-based large-scale weak lensing surveys are planned. Using the Fourier_Quad method, we quantify the shear recovery error as a function of the sampling factor Q, i.e., the ratio between the FWHM of the point-spread-function (PSF) and the pixel size of the CCD, for different PSFs and galaxies of different sizes and noise levels. We show that sub-percent-level accuracy in shear recovery is achievable with single-exposure images for $Q\lesssim 2$. The conclusion holds for galaxies much smaller than the PSF, and those with a significant level of noise.

astro-ph.CO↗