Residual noise covariance for Planck low-resolution data analysis

TL;DR

This paper develops analytical tools and validation methods to estimate residual noise covariance in Planck low-resolution maps, crucial for accurate large-scale anisotropy analysis.

Contribution

It introduces a method to compute and validate residual noise covariance matrices for Planck low-resolution maps, improving noise characterization accuracy.

Findings

Excellent agreement between analytical covariance matrices and Monte Carlo simulations.

Destriping map-makers' noise agreement depends on knee frequency and baseline length.

Proper window functions are essential to minimize aliasing in resolution downgrading.

Abstract

Aims: Develop and validate tools to estimate residual noise covariance in Planck frequency maps. Quantify signal error effects and compare different techniques to produce low-resolution maps. Methods: We derive analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of map-making approaches. We test these analytical predictions using Monte Carlo simulations and their impact on angular power spectrum estimation. We use simulations to quantify the level of signal errors incurred in different resolution downgrading schemes considered in this work. Results: We find an excellent agreement between the optimal residual noise covariance matrices and Monte Carlo noise maps. For destriping map-makers, the extent of agreement is dictated by the knee frequency of the correlated noise component and the chosen baseline offset length. The significance of signal striping is shown to be insignificant when properly dealt with. In map resolution downgrading, we find that a carefully selected window function is required to reduce aliasing to the sub-percent level at multipoles, ell > 2Nside, where Nside is the HEALPix resolution parameter. We show that sufficient characterization of the residual noise is unavoidable if one is to draw reliable contraints on large scale anisotropy. Conclusions: We have described how to compute the low-resolution maps, with a controlled sky signal level, and a reliable estimate of covariance of the residual noise. We have also presented a method to smooth the residual noise covariance matrices to describe the noise correlations in smoothed, bandwidth limited maps.