Recovering the CMB Signal with Machine Learning

Abstract

The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious foreground contaminations from several sources, such as galactic synchrotron and thermal dust emissions. Here, we build a deep convolutional neural network (CNN) to recover the tiny CMB signal from various huge foreground contaminations. Focusing on the CMB temperature fluctuations, we find that the CNN model can successfully recover the CMB temperature maps with high accuracy, and that the deviation of the recovered power spectrum $C_{\ell }$ is smaller than the cosmic variance at $\ell >10$. We then apply this method to the current Planck observation, and find that the recovered CMB is quite consistent with that disclosed by the Planck collaboration, which indicates that the CNN method can provide a promising approach to the component separation of CMB observations. Furthermore, we test the CNN method with simulated CMB polarization maps based on the CMB-S4 experiment. The result shows that both the EE and BB power spectra can be recovered with high accuracy. Therefore, this method will be helpful for the detection of primordial gravitational waves in current and future CMB experiments. The CNN is designed to analyze two-dimensional images, thus this method is not only able to process full-sky maps, but also partial-sky maps. Therefore, it can also be used for other similar experiments, such as radio surveys like the Square Kilometer Array.

20 pages, 25 figures, and 3 tables, updated citations in section 1. The code repository is available at https://github.com/Guo-Jian-Wang/cmbNNCS

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• SPT-3G: A Next-Generation Cosmic Microwave Background Polarization Experiment on the South Pole Telescope
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Cited by in corpus (4)

• Machine Learning for Observational Cosmology
• Likelihood-free Inference with Mixture Density Network
• Recovering Cosmic Microwave Background Polarization Signals with Machine Learning
• Comparing simulated Milky Way satellite galaxies with observations using unsupervised clustering