Title: Using photometric redshift data to improve the detection of galactic filaments with the Bisous model Show Chinese title

Title: 使用测光红移数据通过Bisous模型提高星系丝状结构的检测

Abstract: Filament finders are limited, among other things, by the abundance of spectroscopic redshift data. As there are proportionally more photometric redshift data than spectroscopic, we aim to use photometric data to improve and expand the areas where we can detect the large-scale structure of the Universe. We present a proof of concept, showing that the Bisous filament finder can improve the detected filamentary network with photometric redshift data. We created mock data from the MultiDark-Galaxies catalogue. Galaxies with spectroscopic redshifts were given exact positions from the simulation. Galaxies with photometric redshifts were given uncertainties along one coordinate. The errors were generated with different Gaussian distributions for different samples. There are three different types of samples: spectroscopic only, photometric only, and mixed samples of galaxies with photometric and spectroscopic redshifts. In photometric-only samples, the larger the uncertainty for photometric redshifts, the fewer filaments are detected, and the filaments strongly align along the line of sight. Using mixed samples improves the number of filaments detected and decreases the alignment bias of those filaments. The results are compared against the full spectroscopic sample. The recall for photometric-only samples depends heavily on the size of uncertainty and dropped close to 20%; for mixed samples, the recall stayed between 40% and 80%. The false discovery rate stayed below 5% in every sample tested in this work. Mixed samples showed better results than corresponding photometric-only or spectroscopic-only samples for every uncertainty size and number of spectroscopic galaxies in mixed samples. Mixed samples of galaxies with photometric and spectroscopic redshifts help us to improve and extend the large-scale structure further than possible with only spectroscopic samples. Show Chinese abstract

Abstract: 丝状结构探测器受到多种因素的限制，其中包括光谱红移数据的丰富程度。由于光度红移数据比例上多于光谱红移数据，我们旨在利用光度数据来改进和扩展我们可以检测宇宙大尺度结构的区域。我们提出一个概念验证，表明Bisous丝状结构探测器可以利用光度红移数据改进检测到的丝状网络。我们从MultiDark-Galaxies目录中创建了模拟数据。具有光谱红移的星系被赋予了来自模拟的精确位置。具有光度红移的星系被赋予了一个坐标上的不确定性。这些误差是根据不同样本的不同高斯分布生成的。有三种不同类型的样本：仅光谱样本、仅光度样本以及包含光度和光谱红移星系的混合样本。在仅光度样本中，光度红移的不确定性越大，检测到的丝状结构越少，并且丝状结构强烈沿着视线方向对齐。使用混合样本可以提高检测到的丝状结构数量，并减少这些丝状结构的对齐偏差。结果与完整的光谱样本进行了比较。仅光度样本的召回率严重依赖于不确定性的大小，并下降到20%以下；对于混合样本，召回率保持在40%到80%之间。在本工作中测试的每个样本中，误发现率都低于5%。对于每个不确定度大小和混合样本中的光谱星系数量，混合样本的结果都优于相应的仅光度或仅光谱样本。包含光度和光谱红移的星系混合样本帮助我们进一步改进和扩展大尺度结构，这比仅使用光谱样本更有可能实现。