Cosmic voids are a key component of the large-scale structure that contain a plethora of cosmological information. Typically, voids are identified from the underlying galaxy distribution, which is a biased tracer of the total matter field. Previous works have shown that 2D voids identified in weak lensing maps -- weak lensing voids -- correspond better to true underdense regions along the line of sight. In this work, we study how the properties of weak lensing voids depend on the choice of void finder, by adapting several popular void finders. We present and discuss the differences between identifying voids directly in the convergence maps, and in the distribution of weak lensing peaks. Particular effort has been made to test how these results are affected by galaxy shape noise, which is a dominant source of noise in weak lensing observations. By studying the signal-to-noise ratios (SNR) for the tangential shear profile of each void finder, we find that voids identified directly in the convergence maps have the highest SNR but are also the ones most affected by galaxy shape noise. Troughs are least affected by noise, but also have the lowest SNR. The tunnel algorithm, which identifies voids in the distribution of weak lensing peaks, represents a good compromise between finding a large tangential shear SNR and mitigating the effect of galaxy shape noise.

中文翻译：

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弱透镜图中的最佳空洞探测器

宇宙空洞是包含大量宇宙学信息的大尺度结构的关键组成部分。通常，空隙是从底层星系分布中识别出来的，它是总物质场的一个有偏差的示踪剂。以前的工作表明，弱透镜图中识别出的二维空洞——弱透镜空洞——更符合视线中真正的低密度区域。在这项工作中，我们通过调整几种流行的空隙探测器来研究弱透镜空隙的特性如何取决于空隙探测器的选择。我们提出并讨论了在会聚图中直接识别空隙与弱透镜峰分布之间的差异。已经做出特别努力来测试这些结果如何受到星系形状噪声的影响，星系形状噪声是弱透镜观测中的主要噪声源。通过研究每个寻空器切向剪切剖面的信噪比 (SNR)，我们发现会聚图中直接识别的空隙具有最高的信噪比，但也是受星系形状噪声影响最大的空隙。波谷受噪声影响最小，但信噪比最低。隧道算法识别弱透镜峰分布中的空隙，代表了找到大切向剪切 SNR 和减轻星系形状噪声影响之间的良好折衷。