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arXiv 2004.11507astro-ph.CO
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使用机器学习的全天空宇宙微波背景前景清理

Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning

Matthew A. Petroff, Graeme E. Addison, Charles L. Bennett, Janet L. Weiland

摘要:

为了从毫米和亚毫米天空的观测中提取宇宙学信息,必须首先去除前景成分以产生对宇宙微波背景(CMB)的估计。我们开发了一种机器学习方法,用于毫米和亚毫米天空的全天空温度图。我们构建了一个贝叶斯球形卷积神经网络架构,以生成一个模型,该模型可以捕获前景的光谱和形态学方面。此外,该模型输出每像素的误差估计,其中包括统计和模型的不确定性。然后使用模拟训练该模型,该模拟结合了普朗克卫星发射时可用的这些前景组件的知识。在模拟的地图上,在用预测的标准误差$>50μ$ K掩蔽地图像素之后,在整个天空上用平均绝对差$<4μ$ K恢复CMB;角功率谱也被精确地恢复。一旦通过模拟验证,该模型被应用于普朗克温度观测,从70 GHz到857 GHz的通道,以产生一个前景清洁的CMB地图,在一个Healpix地图分辨率为NSIDE=512。此外,我们证明了该技术的实用性,以评估如何以及不同的模拟相匹配的意见,特别是在热尘埃的建模。

英文摘要:

In order to extract cosmological information from observations of the millimeter and submillimeter sky, foreground components must first be removed to produce an estimate of the cosmic microwave background (CMB). We developed a machine-learning approach for doing so for full-sky temperature maps of the millimeter and submillimeter sky. We constructed a Bayesian spherical convolutional neural network architecture to produce a model that captures both spectral and morphological aspects of the foregrounds. Additionally, the model outputs a per-pixel error estimate that incorporates both statistical and model uncertainties. The model was then trained using simulations that incorporated knowledge of these foreground components that was available at the time of the launch of the Planck satellite. On simulated maps, the CMB is recovered with a mean absolute difference of $<4μ$K over the full sky after masking map pixels with a predicted standard error of $>50μ$K; the angular power spectrum is also accurately recovered. Once validated with the simulations, this model was applied to Planck temperature observations from its 70GHz through 857GHz channels to produce a foreground-cleaned CMB map at a Healpix map resolution of NSIDE=512. Furthermore, we demonstrate the utility of the technique for evaluating how well different simulations match observations, particularly in regard to the modeling of thermal dust.

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