发表机构
School of Information Engineering, Kunming University; School of Computer of Science and Information Engineering, Anyang Institute of Technology; Yunnan Observatories, Chinese Academy of Sciences; Fujian Polytechnic Normal University; College of Big Data, Yunnan Agricultural University（昆明大学信息工程学院; 安阳工学院计算机科学与信息工程学院; 中国科学院云南天文台; 福建技术师范学院; 云南农业大学大数据学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对天文图像对齐中FWHM和SNR参数手动选择低效且不客观的问题，提出基于贝叶斯优化和分层搜索的自适应参数优化方法，自动确定最优参数组合，实现亚像素精度并显著提升对齐自动化水平和成功率。
AI 中文摘要
天文图像的对齐和叠加是探测暗弱天体和进行高精度天体测量的基本步骤。在传统的对齐工作流程中，源列表的提取关键依赖于半高全宽（FWHM）和信噪比（SNR）阈值等关键参数。这些参数通常通过低效的试错过程手动选择，缺乏客观性，且不能保证获得最优结果。我们提出了一种基于贝叶斯优化（BO）的自适应天文图像对齐参数优化方法。我们将参数搜索构建为一个优化问题，目标函数设计为最大化成功匹配的源对数量。通过采用分层搜索策略，我们对FWHM和SNR进行高效的全局搜索，以自动确定针对给定观测数据集的最优参数组合。实验结果表明，我们的方法能够有效处理具有不同视宁度条件和背景噪声水平的图像数据。它快速收敛到一组稳健的对齐参数，实现亚像素精度，并显著提高了对齐过程的自动化水平和成功率。这项工作可为开发大规模、自动化的天文数据处理流水线提供有用的基础。
英文摘要
The alignment and stacking of astronomical images are fundamental steps for detecting faint objects and performing high?precision astrometry. In traditional alignment workflows, the extraction of source lists is critically dependent on key parameters such as the Full Width at Half Maximum (FWHM) and the Signal-to-Noise Ratio (SNR) threshold. These parameters are often selected manually through an inefficient trial-error process that lacks objectivity and does not guarantee optimal results. We present an adaptive method for optimizing astronomical image alignment parameters based on Bayesian Optimization (BO). We frame the parameter search as an optimization problem, with an objective function designed to maximize the number of successfully matched source pairs. By employing a hierarchical search strategy, we perform an efficient global search for FWHM and SNR to automatically determine the optimal combination for a given observational dataset. Experimental results demonstrate that our method effectively handles image data with varying seeing conditions and back?ground noise levels. It rapidly converges to a robust set of alignment parameters, achieving sub-pixel accuracy and significantly improving the automation level and success rate of the alignment process. This work may provide a useful basis for developing large-scale, automated astronomical data processing pipelines
Comments10 pages,6 figures
