DeepSZ: Identification of Sunyaev-Zel’dovich galaxy clusters using deep learning
DeepSZ: Identification of Sunyaev-Zel’dovich galaxy clusters using deep learning
复制标题
DeepSZ：利用深度学习识别 Sunyaev-Zelâdovich 星系团
DOI：
10.1093/mnras/stab2229
复制
发表时间：
2021
影响因子：
4.8
通讯作者：
Nord, B
中科院分区：
文献类型：
--
作者：
Lin, Z;Huang, N;Avestruz, C;Wu, W L;Trivedi, S;Caldeira, J;Nord, B
通过Sunyaev-Zel'dovich（SZ）效应识别的星系团是多波长星系团宇宙学的关键组成部分。我们提出并比较了三种聚类识别方法：SZ聚类发现中的标准匹配滤波器（MF）方法，卷积神经网络（CNN）和“组合”标识符。我们将这些方法应用于模拟毫米地图的几个观测频率的调查类似的SPT-3G，第三代相机的南极望远镜。MF需要图像预处理来去除点源和噪声模型，而CNN只需要很少的图像预处理。此外，CNN需要调整模型中的超参数，并将天空的剪切图像作为输入，识别剪切是否包含集群。我们比较纯度和完整性的差异。MF的信噪比取决于质量和红移。我们的CNN针对给定的质量阈值进行了训练，捕获了与MF不同的一组聚类，其中一些聚类的信噪比低于MF检测阈值。然而，CNN倾向于错误分类其聚类位于切口边缘附近的切口，这可以通过交错切口来减轻。我们利用这两种方法的互补性，结合每种方法的得分进行识别。MF的纯度和完整性均为0.61，CNN的纯度和完整性均为0.59和0.61。组合方法产生0.60和0.77，在纯度适度降低的情况下显著增加了完整性。我们提倡使用组合方法来增加许多低信噪比聚类的置信度。
Galaxy clusters identified via the Sunyaev–Zel’dovich (SZ) effect are a key ingredient in multiwavelength cluster cosmology. We present and compare three methods of cluster identification: the standard matched filter (MF) method in SZ cluster finding, a convolutional neural networks (CNN), and a ‘combined’ identifier. We apply the methods to simulated millimeter maps for several observing frequencies for a survey similar to SPT-3G, the third-generation camera for the South Pole Telescope. The MF requires image pre-processing to remove point sources and a model for the noise, while the CNN requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes cut-out images of the sky as input, identifying the cut-out as cluster-containing or not. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some with signal-to-noise-ratio below the MF detection threshold. However, the CNN tends to mis-classify cut-out whose clusters are located near the edge of the cut-out, which can be mitigated with staggered cut-out. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness are both 0.61 for MF, and 0.59 and 0.61 for CNN. The combined method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many low signal-to-noise clusters.