论文标题
自避免的组件分离，用于亚层典型的微毫米天空
Self-supervised component separation for the extragalactic submillimeter sky
论文作者
论文摘要
我们使用一种基于最初应用于透明度分离的自我审议的深度学习网络的新方法，以同时提取外层次亚略微计的天空的组件，即宇宙微波背景（CMB），宇宙红外背景（CIB），Sunyaev-Zel'dovich（sszel'dovich（szel'dovich）（SZSZ）（SZ）效应。在本概念验证的论文中，我们在Webky临时模拟图上测试了我们在93到545 GHz的频率的方法，并与SZ的最先进的传统方法Milca进行了比较。我们首先在视觉上比较图像，然后统计地分析具有功率光谱的完整重建高分辨率地图。我们研究了其他具有跨光谱的组件的污染，特别强调了CIB与SZ效应之间的相关性，并计算了星系簇位置周围的SZ通量。独立网络学习如何用比MILCA更少污染的不同组件重建不同的组件。尽管此处在理想情况下（无噪声，梁或前景）进行了测试，但该方法显示出在未来实验中（例如Simons天文台（SO））与Planck卫星结合使用的显着潜力。
We use a new approach based on self-supervised deep learning networks originally applied to transparency separation in order to simultaneously extract the components of the extragalactic submillimeter sky, namely the cosmic microwave background (CMB), the cosmic infrared background (CIB), and the Sunyaev-Zel'dovich (SZ) effect. In this proof-of-concept paper, we test our approach on the WebSky extragalactic simulation maps in a range of frequencies from 93 to 545 GHz, and compare with one of the state-of-the-art traditional methods, MILCA, for the case of SZ. We first visually compare the images, and then statistically analyse the full-sky reconstructed high-resolution maps with power spectra. We study the contamination from other components with cross spectra, and particularly emphasise the correlation between the CIB and the SZ effect and compute SZ fluxes around positions of galaxy clusters. The independent networks learn how to reconstruct the different components with less contamination than MILCA. Although this is tested here in an ideal case (without noise, beams, or foregrounds), this method shows significant potential for application in future experiments such as the Simons Observatory (SO) in combination with the Planck satellite.
