AI 中文总结
该研究针对卡西尼-ISS图像背景估计的传统方法缺陷，提出基于DDPM的深度学习框架，在环隙背景估计及卫星质心定位上精度显著提升，可应用于多天文任务。
AI 中文摘要
精确的背景强度估计对天文成像中的高精度天体测量至关重要，尤其在卡西尼成像科学子系统（ISS）观测土星环系统等复杂场景中。传统方法如多项式拟合、统计方法，因假设不匹配且依赖先验知识，在土星环、散射光导致的非均匀条件下常失效，导致估计有偏差、泛化性差。本文提出一种基于去噪扩散概率模型（DDPM）的深度学习框架以解决这些挑战。通过学习噪声模式并迭代重构背景，DDPM对ISS图像环隙区域的背景强度估计较多项式拟合提升最高达62%；此外，将基于DDPM的背景估计应用于环隙中未分辨卫星的质心计算时，在行方向的位置精度提升约14%，在样本方向提升约15%。该框架可自主捕获空间相关性，无需手动调参，且能在多样背景间泛化，这些特性使其成为天体测量、测光、源检测等背景估计任务中颇具前景的轻假设解决方案，有望应用于系外行星凌星成像、深场巡天及未来任务。
英文摘要
Accurate background intensity estimation is crucial for precise astrometric measurements in astronomical imaging, particularly in complex scenarios such as those encountered in Cassini Imaging Science Subsystem (ISS) observations of Saturn's ring system. Traditional methods, like polynomial fitting and statistical method, often fail in non-uniform conditions, such as those caused by Saturn's rings or scattered light, due to mismatched assumptions and reliance on prior knowledge. This results in biased estimates and poor generalizability. We propose a deep learning framework based on Denoising Diffusion Probabilistic Model (DDPM) to address these challenges. By learning noise patterns and iteratively reconstructing backgrounds, DDPM improve background intensity estimation in ring-gap regions of ISS images by up to 62% relative to polynomial fitting. Additionally, When applied to centroiding of unresolved satellites in ring-gap, DDPM-based background estimation enhances positional precision by about 14% in the line direction and 15% in the sample direction. The framework autonomously captures spatial correlations, requires no manual parameter tuning, and generalizes across diverse backgrounds. These characteristics make it a promising, assumption-light solution for background estimation tasks in astrometry, photometry, and source detection, with potential applications to exoplanet transit imaging, deep-field surveys, and future missions.
Comments10 pages, 4 figures, 4 tables
Journal refYongxin Chen et al 2026 AJ 171 107
