Aperture Photometric Accuracy of Point-spread-function-deconvolved Astronomical Images

@misc{zhang2026aperture,
  title = {{Aperture Photometric Accuracy of Point-spread-function-deconvolved Astronomical Images}},
  author = {Shen Zhang and Lei Wang and Yachong Diao and Zhaojun Yan and Xiyan Peng and Yao Liu and Huanyuan Shan and Guanghuan Wang and Fengshan Liu and Chengliang Wei and Lin Nie and Xuechun Chen and Hengkai Ding and Zhenya Zheng},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.01670},
  eprint = {2611.01670},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.01670},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - Aperture Photometric Accuracy of Point-spread-function-deconvolved Astronomical Images
AU  - Shen Zhang
AU  - Lei Wang
AU  - Yachong Diao
AU  - Zhaojun Yan
AU  - Xiyan Peng
AU  - Yao Liu
AU  - Huanyuan Shan
AU  - Guanghuan Wang
AU  - Fengshan Liu
AU  - Chengliang Wei
AU  - Lin Nie
AU  - Xuechun Chen
AU  - Hengkai Ding
AU  - Zhenya Zheng
PY  - 2026
DA  - 2026/11/01
PB  - arXiv
DO  - 10.48550/arxiv.2611.01670
UR  - https://arxiv.org/abs/2611.01670
AB  - This paper introduces an improved Richardson--Lucy (RL) framework utilizing negative backfilling (NB) and the novel filtered image deconvolution and noise backfilling (FiDNoBa) algorithm. By integrating a low-pass prefiltering stage---specifically employing a Kalman filter---with the reintegration of filtered-out noise components after RL iterations, we effectively suppress noise artifacts while maintaining rigorous flux integrity. We extensively evaluate our Kalman + RL + NB approach using both numerical simulations and real observational data. Simulations across three regimes demonstrate superior photometric accuracy, reducing the relative mean deviation (RMD) to below 1\% for isolated sources, maintaining high fidelity (RMD ∼0.37\%) for blended Sérsic galaxies, and achieving ∼5\% RMD in crowded stellar fields while significantly increasing source recovery rates compared to traditional point-spread function photometry. Crucially, we validate the practical efficacy of FiDNoBa on real space-based (JWST/NIRCam) and ground-based (Very Large Telescope Survey Telescope OmegaCAM) images. The results confirm that our method reliably handles complex instrumental signatures, such as the extended wings of the STPSF, mitigating the severe ringing artifacts typical of traditional RL deconvolution while ensuring highly stable aperture photometry in practical survey processing.
ER  -