AI 中文总结
本文重新设计ADP聚类算法用于天文源去混叠，通过真实模拟和Euclid数据验证，在保持测光精度的同时实现12-156倍加速，为大规模巡天提供高效方案。
AI 中文摘要
源去混叠是当前和未来天文巡天面临的一个基本挑战，其中源密度和图像深度的增加导致重叠探测的数量不断增长。准确的去混叠对于可靠测量源的形态和测光以及宇宙学分析至关重要。我们提出了高级密度峰（ADP）聚类算法的重新设计，专门用于在探测区域内识别和分离混合的天文源。我们开发了一个验证框架，结合了真实的图像模拟、自动生成的地面真值分割和标签不变度量。ADP与已建立的基于密度的天文去混叠器ASTErIsM进行了评估，使用了成对模拟、合成多源图像和Euclid Q1公开数据。在成对模拟中，两种方法在广泛的源间距和通量比范围内表现出相当的性能，测光差异通常低于1%，在最具挑战性的情况下达到5-7%，且没有系统性偏差。在多源模拟中，ADP恢复了约8%更多的地面真值源，而两种方法识别出的源位置在亚像素级别上一致。在Euclid Q1公开数据上，两种方法在分割面积、椭圆率、位置角和测光方面表现出强烈的一致性，对于最小和最暗的源差异最大。ADP还提供了显著的计算优势：在19200 x 19200像素的Euclid图像上的端到端基准测试大约需要16-200秒，相对于ASTErIsM实现了12-156倍的加速，中位数和平均改进分别为36倍和54倍。这些结果表明，ADP以显著较低的计算成本提供了科学上具有竞争力的去混叠，使其成为大规模天文成像巡天的有前景的方法。
英文摘要
Source deblending is a fundamental challenge for current and forthcoming astronomical surveys, where increasing source density and image depth lead to a growing number of overlapping detections. Accurate deblending is essential for reliable measurements of source morphology and photometry, as well as for cosmological analyses. We present a redesign of the Advanced Density Peak (ADP) clustering algorithm, tailored to the identification and separation of blended astronomical sources within detection regions. We develop a validation framework combining realistic image simulations, automatically generated ground-truth segmentation, and label-invariant metrics. ADP is assessed against ASTErIsM, an established density-based astronomical deblender, using pairwise simulations, synthetic multi-source images, and Euclid Q1 public data. In pairwise simulations, the methods show comparable performance across a broad range of source separations and flux ratios, with photometric differences typically below 1% and reaching 5-7% in the most challenging cases, without systematic bias. In multi-source simulations, ADP recovers approximately 8% more ground-truth sources, while the positions of sources identified by both methods agree at the sub-pixel level. On Euclid Q1 public data, the methods show strong agreement in segmentation area, ellipticity, position angle, and photometry, with the largest differences for the smallest and faintest sources. ADP also provides a substantial computational advantage: end-to-end benchmarks on 19200 x 19200 pixel Euclid images require approximately 16-200 s, corresponding to speedups of 12-156x relative to ASTErIsM, with median and mean improvements of 36x and 54x, respectively. These results show that ADP provides scientifically competitive deblending at substantially lower computational cost, making it a promising approach for large-scale astronomical imaging surveys.
Comments19 pages, 25 figures
