发表机构
Universidad Politécnica de Cartagena; Durham University; Université Paris-Saclay; Université Paris Cité; CEA; CNRS; Instituto de Física de Cantabria; INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna; INFN-Sezione di Bologna; Università di Bologna; INAF, Istituto di Radioastronomia; University of Heidelberg（卡塔赫纳理工大学; 杜伦大学; 巴黎萨克雷大学; 巴黎西岱大学; 法国原子能和替代能源委员会; 法国国家科学研究中心; 坎塔布里亚物理研究所; 博洛尼亚天文物理学与空间科学观测站（意大利国家天体物理研究所）; 博洛尼亚意大利国家核物理研究所; 博洛尼亚大学; 意大利国家天体物理研究所射电天文学研究所; 海德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文评估九种弱透镜星系团探测算法，预选四种互补方法，组合后完备度提升两倍，预计Euclid首期数据可探测约2500个星系团。
AI 中文摘要
弱引力透镜通过直接追踪星系团的总物质含量，为探测星系团提供了一种强有力的方法。其有效性随背景星系密度的增加而提高，而当前大天区巡天的高灵敏度和广覆盖正满足这一要求。一个典型的例子是Euclid望远镜，它将测绘约14000平方度的天空，测量数十亿个星系的形状，为仅通过弱透镜信号探测星系团开辟了有意义的窗口。我们展示了在盲测挑战中九种星系团探测算法的结果，使用了来自DEMNUni-Cov模拟的1200平方度Euclid-like弱透镜观测。这些方法的性能通过将其探测结果与信噪比大于2、满足z-M选择截断的已知合成星系团进行匹配来评估，并采用了两种不同的匹配程序。该挑战的目的是为即将到来的Euclid数据发布识别并改进通过弱透镜探测星系团的策略。我们根据各方法的单独表现和互补性预选了四种方法。每种预选方法采用不同的途径：AMICO-WL使用最优滤波技术，DoG采用高斯滤波，O21依赖孔径质量滤波，W234应用多尺度小波滤波。这些方法的结果可以合并以增强Euclid数据的处理。单独来看，这些算法在平均纯度为90%时达到约10%的完备度，而它们的组合使整体性能提升约两倍，对低红移、高质量星系团的完备度超过70%。将这项工作结果外推到Euclid数据发布1，我们预计将通过弱引力透镜探测到约2500个星系团。
英文摘要
Weak gravitational lensing offers a powerful way to detect galaxy clusters by directly tracing their total matter content. Its effectiveness increases with the density of background galaxies, a requirement that is now being met by the high sensitivity and wide coverage of current large-area surveys. A prime example is the Euclid telescope, which will map approximately 14 000 deg2 of the sky, measuring the shapes of billions of galaxies and opening a meaningful window for detecting galaxy clusters uniquely through their weak lensing signal. We present the results of nine galaxy cluster detection algorithms in a blind challenge, using 1200 deg2 of Euclid-like weak lensing observations from the DEMNUni-Cov simulations. The performance of these methods was assessed by matching their detections to known synthetic clusters with signal-to-noise ratios greater than 2, satisfying the z-M selection cut, and adopting two different matching procedures. The purpose of the challenge was to identify and improve strategies for galaxy cluster detection via weak lensing for the upcoming Euclid data releases. We pre-selected four methods based on their individual performance and complementarity. Each pre-selected method adopts a distinct approach: AMICO-WL uses an optimal filtering technique, DoG employs Gaussian filtering, O21 relies on aperture-mass filtering, and W234 applies multi-scale wavelet filtering. Together, the results of these methods can be merged to enhance the processing of Euclid data. Individually, these algorithms reach approximately 10% completeness for a mean purity of 90%, while their combination leads to roughly a two-fold improvement in overall performance, exceeding 70% completeness for low-redshift, high-mass clusters. When extrapolating the results of this work to Euclid Data Release 1, we expect to detect approximately 2500 galaxy clusters via weak gravitational lensing.
Comments26 pages, 8 figures, accepted for publication in A&A on 16 September 2026
