Paper
CSST-PSFNet: A Point Spread Function Reconstruction Model for the CSST Based on Deep Learning
Authors
Peipei Wang, Peng Wei, Chao Liu, Rui Wang, Feng Wang, Xin Zhang
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.10424v1</id>\n <title>CSST-PSFNet: A Point Spread Function Reconstruction Model for the CSST Based on Deep Learning</title>\n <updated>2026-03-11T05:13:11Z</updated>\n <link href='https://arxiv.org/abs/2603.10424v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.10424v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='astro-ph.IM'/>\n <published>2026-03-11T05:13:11Z</published>\n <arxiv:comment>Accepted for publication in ApJS, 2026</arxiv:comment>\n <arxiv:primary_category term='astro-ph.IM'/>\n <author>\n <name>Peipei Wang</name>\n </author>\n <author>\n <name>Peng Wei</name>\n </author>\n <author>\n <name>Chao Liu</name>\n </author>\n <author>\n <name>Rui Wang</name>\n </author>\n <author>\n <name>Feng Wang</name>\n </author>\n <author>\n <name>Xin Zhang</name>\n </author>\n </entry>"
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