Aperture Photometric Accuracy of Point-spread-function-deconvolved Astronomical Images
Shen Zhang, Lei Wang, Yachong Diao, Zhaojun Yan, Xiyan Peng, Yao Liu, Huanyuan Shan, Guanghuan Wang, Fengshan Liu, Chengliang Wei, Lin Nie, Xuechun Chen, Hengkai Ding, Zhenya Zheng
Abstract
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.
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