A Constrained NILC method for CMB B mode observations

TL;DR

This paper introduces a constrained NILC method that improves the extraction of CMB B-mode signals at low SNR by incorporating prior foreground information, significantly reducing bias in tensor-to-scalar ratio measurements.

Contribution

The paper develops a modified constrained NILC method that enhances low SNR CMB B-mode analysis and demonstrates its effectiveness with mock data, reducing bias compared to standard NILC.

Findings

Reduced foreground residuals in mock data analysis.

Bias in tensor-to-scalar ratio is decreased from 0.05 to 0.005.

Method performs well with conservative noise levels.

Abstract

The Internal Linear Combination (ILC) method is commonly employed to extract the cosmic microwave background (CMB) signal from multi-frequency observation maps. However, the performance of the ILC method tends to degrade when the signal-to-noise ratio (SNR) is relatively low, particularly when measuring the primordial $B$-modes to detect the primordial gravitational waves. To address this issue, an enhanced version of the ILC method, known as constrained ILC, is proposed. This method is designed to be more suitable for situations with low signal-to-noise ratio (SNR) by incorporating additional prior foreground information. In our study, we have modified the constraint Needlet ILC method and successfully improved its performance at low SNR. We illustrate our methods using mock data generated from the combination of WMAP, Planck and a ground-based experiment in the northern hemisphere, and the chosen noise level for the ground-based experiment are very conservative which can be easily achieved in the very near future. The results show that the level of foreground residual can be well controlled. In comparison to the standard NILC method, which introduces a bias to the tensor-to-scalar ratio ($r$) of approximately $0.05$, the constrained NILC method exhibits a significantly reduced bias of only around $5\times10^{-3}$ towards $r$ which is much smaller than the statistical error.