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arXiv 2302.14724astro-ph.CO
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CMB温度各向异性图中点源检测的机器学习方法

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

P. Diego-Palazuelos, R. B. Barreiro, P. Vielva, D. Balbás, M. López-Caniego, D. Herranz, B. Casaponsa

摘要:

我们提出了一种机器学习的方法来盲检测河外点源的宇宙微波背景的温度各向异性的地图。使用普朗克所看到的微波天空的真实模拟,我们训练一个卷积神经网络(CNN),将源检测作为图像分割问题来解决。我们将天空划分为银河前景强度逐渐增加的区域,并为每个区域独立地训练专门的CNN。这种策略导致了有希望的完整性和可靠性水平,我们的CNN大大优于传统的检测方法,如在接近银河平面的区域的匹配滤波器。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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