Foreground removal from CMB temperature maps using an MLP neural network

TL;DR

This paper explores using a multilayer perceptron neural network to effectively remove galactic foreground contamination from simulated CMB temperature maps, improving the accuracy of cosmic signal extraction.

Contribution

It demonstrates that a simple neural network can accurately extract CMB temperature signals from contaminated maps, covering most of the sky and handling the full noise range.

Findings

Neural network achieves high correlation with true CMB signals.

Method covers over 80% of the sky with reliable estimates.

Single network adapts to Planck noise levels across the sky.

Abstract

One of the main obstacles in extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the galactic foregrounds simple, power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined CMB and foreground signals has been investigated. As a specific example, we have analysed simulated data, like that expected from the ESA Planck Surveyor mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates, over more than 80 percent of the sky, that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.