发表机构
University of Cincinnati; LPSC-IN2P3, Univ. Grenoble Alpes, CNRS; Istituto Nazionale di Astrofisica; Stanford University; Université Paris-Saclay, CEA, IRFU; University of Michigan; Space Telescope Science Institute; Université Paris Cité, CNRS/IN2P3, APC; Duke University（辛辛那提大学; 格勒诺布尔阿尔卑斯大学IN2P3粒子与核物理实验室CNRS; 意大利国家天体物理研究所; 斯坦福大学; 巴黎-萨克雷大学CEA法国原子能和替代能源委员会IRFU; 密歇根大学; 太空望远镜科学研究所; 巴黎西岱大学CNRS/IN2P3APC实验室; 杜克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出混叠熵度量以量化LSST星系图像混叠，通过阈值筛选减少混叠对星系团透镜测量及宇宙学参数估计的系统误差。
AI 中文摘要
下一代巡天项目，如维拉·C·鲁宾天文台的时空遗产巡天（LSST），将提供前所未有的深度和天区覆盖，从而能够对弱引力透镜和星系聚集等宇宙学探针进行精确测量。然而，成像深度的增加会导致星系图像发生显著混叠，尤其是在星系团等致密天区。这种混叠现象，加之地面观测中大气模糊的影响，会污染星系属性测量并导致源混淆。为了同时捕捉这些效应，我们开发了一个概率框架，引入了混叠熵这一度量，用于量化将探测到的天体与真实星系或外部参考源匹配时的模糊性。利用DESC数据挑战2（DC2）的模拟数据，我们刻画了LSST数据中的混叠特征，并量化了其对cosmoDC2暗晕周围星系团透镜宇宙学的影响。我们证明，施加混叠熵阈值$S_b<0.2$能有效滤除高度混叠的天体（约25%），这些天体在巡天极限星等附近尤为普遍，并且与形状测量和测光红移的较高误差相关。应用这一截断显著降低了星系团透镜轮廓和质量估计中由混叠引起的偏差，从而减轻了宇宙学参数的系统误差——最显著的是降低了$\u03c3_8$估计中的张力。我们的方法可轻松推广到其他静态探针，并为真实数据分析提供了一条实用路径，尤其是在利用来自欧几里得（Euclid）或罗曼太空望远镜（Roman Space Telescope）等天基任务的重叠高分辨率数据集时，这些外部数据集可作为参考星表，以改善对LSST数据中混叠源的识别。
英文摘要
Next-generation galaxy surveys, like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will deliver unprecedented depth and sky coverage, enabling precise measurements of cosmic probes such as weak lensing and galaxy clustering. However, increased imaging depth leads to significant blending of galaxy images, particularly in dense fields like galaxy clusters. This blending, exacerbated by atmospheric blurring in ground-based observations, contaminates galaxy property measurements and causes source confusion. To simultaneously capture these effects, we develop a probabilistic framework introducing the blending entropy, a metric quantifying the ambiguity in matching detected objects to true galaxies or external reference sources. Using simulated data from the DESC Data Challenge 2 (DC2), we characterize blending in LSST data and quantify its impact on cluster lensing cosmology around cosmoDC2 halos. We demonstrate that imposing a blending entropy threshold of $S_b<0.2$ effectively filters out highly blended objects (around 25%), which are especially prevalent near the survey's magnitude limit and are associated with higher errors in shape measurements and photometric redshifts. Applying this cut substantially reduces blending-induced biases in cluster lensing profiles and mass estimates, thereby mitigating systematic errors in cosmological parameters---most notably reducing tension in $σ_8$ estimates. Our method is readily generalizable to other static probes and offers a practical path forward for real data analyses, particularly when leveraging overlapping high-resolution datasets from spaced-based missions such as Euclid or the Roman Space Telescope, where these external datasets can act as reference catalogs to improve the identification of blended sources in LSST data.
Comments31 pages, 23 figures, 4 tables
