Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Abstract
The detection limit of astronomical imaging observations is limited by several noise sources. Some of that noise is correlated between neighbouring image pixels and exposures, so in principle could be learned and corrected. We present an astronomical self-supervised transformer-based denoising algorithm (ASTERIS), that integrates spatiotemporal information across multiple exposures. Benchmarking on mock data indicates that ASTERIS improves detection limits by 1.0 magnitude at 90% completeness and purity, while preserving the point spread function and photometric accuracy. Observational validation using data from the James Webb Space Telescope (JWST) and Subaru telescope identifies previously undetectable features, including low-surface-brightness galaxy structures and gravitationally-lensed arcs. Applied to deep JWST images, ASTERIS identifies three times more redshift > 9 galaxy candidates, with rest-frame ultraviolet luminosity 1.0 magnitude fainter, than previous methods.
Cite
@article{arxiv.2602.17205,
title = {Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising},
author = {Yuduo Guo and Hao Zhang and Mingyu Li and Fujiang Yu and Yunjing Wu and Yuhan Hao and Song Huang and Yongming Liang and Xiaojing Lin and Xinyang Li and Jiamin Wu and Zheng Cai and Qionghai Dai},
journal= {arXiv preprint arXiv:2602.17205},
year = {2026}
}
Comments
Published in Science. This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution