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Autori principali: Zhang, Shen, Wang, Lei, Shan, Huanyuan, Li, Ran, Cao, Xiaoyue, Gao, Yunhao
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2511.09881
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author Zhang, Shen
Wang, Lei
Shan, Huanyuan
Li, Ran
Cao, Xiaoyue
Gao, Yunhao
author_facet Zhang, Shen
Wang, Lei
Shan, Huanyuan
Li, Ran
Cao, Xiaoyue
Gao, Yunhao
contents We propose fiDrizzleMU, an algorithm for co-adding exposures via iterative multiplicative updates, replacing the additive correction framework. This method achieves superior anti-aliasing and noise reduction in stacked images. When applied to James Webb Space Telescope data, the fiDrizzleMU algorithm reconstructs a gravitational lensing candidate that was significantly blurred by the pipeline's resampling process. This enables the accurate recovery of faint and extended structures in high-resolution astronomical imaging.
format Preprint
id arxiv_https___arxiv_org_abs_2511_09881
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle fiDrizzle-MU: A Fast Iterative Drizzle with Multiplicative Updates
Zhang, Shen
Wang, Lei
Shan, Huanyuan
Li, Ran
Cao, Xiaoyue
Gao, Yunhao
Instrumentation and Methods for Astrophysics
We propose fiDrizzleMU, an algorithm for co-adding exposures via iterative multiplicative updates, replacing the additive correction framework. This method achieves superior anti-aliasing and noise reduction in stacked images. When applied to James Webb Space Telescope data, the fiDrizzleMU algorithm reconstructs a gravitational lensing candidate that was significantly blurred by the pipeline's resampling process. This enables the accurate recovery of faint and extended structures in high-resolution astronomical imaging.
title fiDrizzle-MU: A Fast Iterative Drizzle with Multiplicative Updates
topic Instrumentation and Methods for Astrophysics
url https://arxiv.org/abs/2511.09881