Foreground removal from WMAP 5yr temperature maps using an MLP neural network

Abstract

One of the main obstacles for extracting the cosmic microwave background (CMB) signal from observations in the mm/sub-mm range is the foreground contamination by emission from Galactic component: mainly synchrotron, free-free, and thermal dust emission. The statistical nature of the intrinsic CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5yr temperature data without using any auxiliary data. A simple \emph{multilayer perceptron} neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also, the systematic errors, i.e.\ errors correlated with the Galactic foregrounds, are very small. With these results the neural network method is well prepared for dealing with the high - quality CMB data from the ESA Planck Surveyor satellite.

6 pages, 13 figures

References in corpus (6)

• Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Likelihoods and Parameters from the WMAP data
• Three-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Temperature Analysis
• Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Angular Power Spectra
• A full sky, low foreground, high resolution CMB map from WMAP
• Component separation methods for the Planck mission
• Foreground removal from CMB temperature maps using an MLP neural network

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• Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning
• Recovering the CMB Signal with Machine Learning
• Local analyses of Planck maps with Minkowski Functionals
• Searching for primordial non-Gaussianity in Planck CMB maps using a combined estimator
• Recent developments in astrophysical and cosmological exploitation of microwave surveys
• Foreground removal from WMAP 7yr polarization maps using an MLP neural network
• Deep Needlet: A CNN based full sky component separation method in Needlet space
• Confirmation of the detection of B-modes in the Planck polarization maps
• Excess B-modes extracted from the Planck polarization maps