CSST-PSFNet: A Point Spread Function Reconstruction Model for the CSST Based on Deep Learning

TL;DR

This paper introduces CSST-PSFNet, a deep learning model that accurately reconstructs the point spread function for the CSST telescope, improving weak lensing measurements and enabling better on-orbit PSF calibration.

Contribution

The paper presents a novel deep learning framework combining residual networks, Transformers, and variational encoding for PSF reconstruction in space telescope imaging.

Findings

Achieves residual size precision below 0.005

Ellipticity residual precision below 0.002

Performs robustly in weak-label adaptation scenarios

Abstract

This paper presents CSST-PSFNet, a deep learning method for high-fidelity point spread function (PSF) reconstruction developed for the Chinese Space Station Survey Telescope (CSST). The model integrates a residual neural network, a lightweight Transformer architecture, and a variational latent representation to address key challenges in CSST imaging, including severe PSF undersampling, inter-band variability, and smooth spatial variation across the focal plane. Trained and validated on high-resolution star-PSF pairs generated by the CSST Main Survey Simulator, CSST-PSFNet achieves improved pixel-level accuracy and more precise recovery of shape parameters relevant to weak lensing compared to widely used PSFEx. On both the standard test dataset and a blurred dataset representing the upper bound of expected on-orbit PSF degradation, the model achieves a size residual precision below 0.005 and an ellipticity residual precision below 0.002. A weak-label adaptation experiment further shows that the model can recover PSFEx-level performance when the true PSF is unknown, demonstrating robustness in controlled degradation scenarios and weak-label adaptation experiments. These results indicate that CSST-PSFNet provides a flexible and extensible framework for future on-orbit PSF calibration in large-scale CSST surveys, with potential applications in weak-lensing cosmology and precision astrophysical measurements.