用自监督时空间去噪提升天文成像探测极限，发现更暗弱的星系结构。
Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
- 基于变压器的自监督算法，融合多帧图像时空信息去噪
- 检测极限提升1.0星等，90%完整性和纯度下仍保持精度
- 适合深空巡天、极暗弱天体探测研究者使用
天文成像的探测极限受多种噪声影响，部分噪声在相邻像素和曝光间存在相关性，理论上可被学习并修正。本文提出一种基于自监督变换器的时空去噪算法（ASTERIS），通过整合多张图像的时空信息实现降噪。在模拟数据上的基准测试表明，该方法在90%完整性和纯度条件下使探测极限提升1.0星等，同时保持点扩散函数和测光精度。基于詹姆斯·韦布空间望远镜（JWST）与昴星团望远镜的实际数据验证，成功识别出此前无法探测的低表面亮度星系结构及引力透镜弧。应用于深场JWST图像时，相比以往方法，可发现三倍数量的红移大于9的星系候选体，其紫外波段本征光度更暗1.0星等。
原文摘要 · Abstract (English)
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.
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