Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising

• 成果类型: Article
• 署名作者: Guo, Yuduo; Zhang, Hao; Li, Mingyu; Yu, Fujiang; Wu, Yunjing; Hao, Yuhan; Huang, Song; Liang, Yongming; Lin, Xiaojing; Li, Xinyang; Wu, Jiamin; Cai, Zheng; Dai, Qionghai
• 署名单位: Tsinghua University; Tsinghua University; Tsinghua University; University of Tokyo; National Institutes of Natural Sciences (NINS) - Japan; National Astronomical Observatory of Japan (NAOJ); University of Tokyo; Tsinghua University; Qinghai University; Tsinghua University
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-8075; 1095-9203
• DOI: 10.1126/science.ady9404
• 发表日期: 2026-04-30
• 页码: eady9404
• 关键词: LUMINOSITY FUNCTION; INFRARED CAMERA; jwst; SEXTRACTOR; BRIGHTNESS; galaxies; stellar; NIRCAM; noise

摘要

The detection limit of astronomical imaging observations is limited by several noise sources. Some of that noise is correlated between neighboring pixels and exposures, so in principle it could be learned and corrected. We present the Astronomical Self-supervised Transformer-based Denoising (ASTERIS) algorithm, which integrates spatiotemporal information across multiple exposures. Benchmarking on mock data indicated 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 the Subaru Telescope identified previously undetectable features, including low-surface-brightness galaxy structures and gravitationally lensed arcs. Applied to deep JWST images, ASTERIS identified three times more redshift greater than or similar to 9 galaxy candidates than previous methods, with rest-frame ultraviolet luminosity 1.0 magnitude fainter.