CO Component Estimation Based on the Independent Component Analysis

TL;DR

This paper demonstrates that FastICA can effectively separate CO line emissions from microwave sky maps, enabling more accurate CMB measurements by removing foreground contamination.

Contribution

It applies FastICA to microwave sky maps for the first time to successfully isolate CO emissions, improving component separation methods.

Findings

FastICA successfully identifies the CO component as the most non-Gaussian.

Subtracting CO and dust components yields unbiased CMB power spectrum estimates.

The method is effective for multiple frequency maps including 100GHz, 143GHz, and 217GHz.

Abstract

Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply the FastICA to the component separation problem of the microwave background including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically we prepare 100GHz, 143GHz, and 217GHz mock microwave sky maps including galactic thermal dust, NANTEN CO line, and the Cosmic Microwave Background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that the FastICA can successfully estimate the CO component as the first independent component in our deflection algorithm as its distribution has the largest degree of non-Gaussianity among the components. By subtracting the CO and the dust components from the original sky maps, we will be able to make an unbiased estimate of the cosmological CMB angular power spectrum.