RPCA: a Resolved PSF reconstruction method for undersampled images

Abstract

The Point-Spread Function (PSF) is a critical component in various astrophysical research areas, including galaxy morphology, photometric redshift determination, and weak lensing studies. As the next generation of space telescopes prepares for operations, including the China Space Station Telescope (CSST), accurate PSF modeling has become increasingly important. However, accurately modeling the PSF remains challenging, particularly for poorly sampled images. To construct reliable PSF models based on star images, we aim to develop a novel and robust method, which accounts for the pixel integration effect in modeling for reconstructing high-resolution PSFs from undersampled or poorly resolved images. In this paper, we present Resolved Principal Components Analysis (RPCA), a novel approach that accounts for pixel integration effects to enable the direct reconstruction of resolved PSFs from undersampled images via an Expectation-Maximization PCA (EMPCA) technique. Our method has been tested on a simulated PSF dataset for the China Space Station Telescope (CSST), where both observational and detector-induced effects were considered during simulation. The results demonstrate that RPCA achieves superior precision in PSF shape parameters compared to existing methods such as Multi-CCD (MCCD), Resolved Components Analysis (RCA), and PSFEx. Notably, RPCA {could reduce the bias of ΔR²/R² by approximately one order of magnitude with respect to MCCD, RCA and PSFEx. RPCA exhibits robust stability under realistic observational conditions, including varying signal-to-noise ratios (SNRs) and contamination from cosmic rays and bad pixels. This suggests that RPCA could be a promising candidate for future CSST PSF modeling efforts.

Scale-Vector Alignment: A Scale-Aware Framework for Spatially Resolved Morphological Similarity in Astronomical Images
Mengke Zhao, Guang-Xing Li, Keping Qiu +1
arXiv21 Sep 2026PreprintarXiv licenceFull textGalaxies Instrumentation & methodsarXiv
Forecasting the Ambient Solar Wind With a Physics-Informed Learned Inner Boundary: Part I-Differentiable Inverse Reconstruction From Solar Images
Enrico Camporeale, Andong Hu, Prateek Mayank +1
arXiv1 Nov 2026PreprintCC BY 4.0Full textSolar & stellar physicsarXiv
GA-NIFS: sRMS, a new method for disentangling resolved and unresolved emission in integral field spectroscopy. Application to distant quasars
Michele Perna, Santiago Arribas, Carlota Prieto-Jiménez +28
arXiv10 Oct 2026PreprintCC BY 4.0Full textGalaxiesarXiv
A deep learning algorithm for black hole spin estimation using hot spot secondary images
A. I. Yfantis, R. Al-Belmpeisi
Astronomy & Astrophysics23 Oct 2026Peer-reviewedPublisher terms1 citedSolar & stellar physicsCrossref
Fast Astronomical Transients in Archival Photographic Plates: Using optical aberrations as a tool for discerning real images, from plate artifacts
Ivo Busko
arXiv8 Oct 2026PreprintCC BY 4.0Full textInstrumentation & methodsarXiv

Cite

BibTeX RIS

BibTeX

@misc{nie2026rpca,
  title = {{RPCA: a Resolved PSF reconstruction method for undersampled images}},
  author = {Lin Nie and Huanyuan Shan and Guoliang Li and Tobías I. Liaudat and Chengliang Wei and Peng Wei and Tianmeng Zhang and Hekun Li and Lei Wang and Han Qu and Zhaojun Yan and Nan Li and You Wu and Peng Jia},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.19909},
  eprint = {2610.19909},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.19909},
}

RIS

TY  - UNPB
TI  - RPCA: a Resolved PSF reconstruction method for undersampled images
AU  - Lin Nie
AU  - Huanyuan Shan
AU  - Guoliang Li
AU  - Tobías I. Liaudat
AU  - Chengliang Wei
AU  - Peng Wei
AU  - Tianmeng Zhang
AU  - Hekun Li
AU  - Lei Wang
AU  - Han Qu
AU  - Zhaojun Yan
AU  - Nan Li
AU  - You Wu
AU  - Peng Jia
PY  - 2026
DA  - 2026/10/14
PB  - arXiv
DO  - 10.48550/arxiv.2610.19909
UR  - https://arxiv.org/abs/2610.19909
AB  - The Point-Spread Function (PSF) is a critical component in various astrophysical research areas, including galaxy morphology, photometric redshift determination, and weak lensing studies. As the next generation of space telescopes prepares for operations, including the China Space Station Telescope (CSST), accurate PSF modeling has become increasingly important. However, accurately modeling the PSF remains challenging, particularly for poorly sampled images. To construct reliable PSF models based on star images, we aim to develop a novel and robust method, which accounts for the pixel integration effect in modeling for reconstructing high-resolution PSFs from undersampled or poorly resolved images. In this paper, we present Resolved Principal Components Analysis (RPCA), a novel approach that accounts for pixel integration effects to enable the direct reconstruction of resolved PSFs from undersampled images via an Expectation-Maximization PCA (EMPCA) technique. Our method has been tested on a simulated PSF dataset for the China Space Station Telescope (CSST), where both observational and detector-induced effects were considered during simulation. The results demonstrate that RPCA achieves superior precision in PSF shape parameters compared to existing methods such as Multi-CCD (MCCD), Resolved Components Analysis (RCA), and PSFEx. Notably, RPCA {could reduce the bias of ΔR^2/R^2 by approximately one order of magnitude with respect to MCCD, RCA and PSFEx. RPCA exhibits robust stability under realistic observational conditions, including varying signal-to-noise ratios (SNRs) and contamination from cosmic rays and bad pixels. This suggests that RPCA could be a promising candidate for future CSST PSF modeling efforts.
ER  -

Abstract

Related

Cite