标题： 机器学习方法在CMB温度各向异性地图中点源的探测中的应用 显示英文标题

标题： Machine learning approach to the detection of point sources in maps of the CMB temperature anisotropies

摘要： 我们提出了一种机器学习方法，用于宇宙微波背景温度各向异性图上河外点源的盲检测。利用普朗克望远镜观测到的微波天空的现实模拟，我们将源检测问题视为图像分割问题，并训练一个卷积神经网络（CNN）。我们将天空划分为银河前景强度逐渐增加的区域，并为每个区域独立训练专门化的CNN。这种方法在接近银道面的区域中达到了令人鼓舞的完整性和可靠性水平，我们的CNN在性能上明显优于传统的匹配滤波器等检测方法。 显示英文摘要

摘要： We propose a machine learning approach to the blind detection of extragalactic point sources on maps of the temperature anisotropies of the cosmic microwave background. Using realistic simulations of the microwave sky as seen by Planck, we train a convolutional neural network (CNN) that solves source detection as an image segmentation problem. We divide the sky into regions of progressively increasing Galactic foreground intensity and independently train specialized CNNs for each region. This strategy leads to promising levels of completeness and reliability, with our CNN substantially outperforming traditional detection methods like the matched filter in regions close to the Galactic plane.