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[2602.17205] Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Summary
The paper presents ASTERIS, a self-supervised spatiotemporal denoising algorithm that enhances detection limits in astronomical imaging, achieving significant improvements in identifying faint cosmic structures.
Why It Matters
This research is crucial as it addresses noise limitations in astronomical imaging, enabling the detection of previously undetectable astronomical features. By improving detection limits, it enhances our understanding of the universe, particularly in identifying distant galaxies and cosmic phenomena.
Key Takeaways
- ASTERIS improves detection limits by 1.0 magnitude at 90% completeness.
- The algorithm preserves photometric accuracy while enhancing imaging capabilities.
- It successfully identifies low-surface-brightness galaxy structures and gravitationally-lensed arcs.
- Applied to JWST data, it triples the number of redshift > 9 galaxy candidates.
- The research demonstrates the potential of self-supervised learning in astrophysics.
Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2602.17205 (astro-ph) [Submitted on 19 Feb 2026] Title:Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising Authors:Yuduo Guo, Hao Zhang, Mingyu Li, Fujiang Yu, Yunjing Wu, Yuhan Hao, Song Huang, Yongming Liang, Xiaojing Lin, Xinyang Li, Jiamin Wu, Zheng Cai, Qionghai Dai View a PDF of the paper titled Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising, by Yuduo Guo and 12 other authors View PDF 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 lumi...