Blind Map Level Systematics Cleaning: A Quadratic Estimator Approach

TL;DR

This paper introduces a quadratic estimator-based method for diagnosing and removing systematics in CMB maps, particularly temperature to polarization leakage, improving the accuracy of primordial B-mode signal detection.

Contribution

It presents a novel iterative quadratic estimator approach with Gaussian filtering for systematic cleaning, validated on simulations, achieving unbiased tensor-to-scalar ratio measurements.

Findings

Systematic B-mode power reduced by nearly two orders of magnitude.

Method validated on both idealized and realistic simulations.

Achieves unbiased measurement of tensor-to-scalar ratio r.

Abstract

We present the first detailed case study using quadratic estimators (QE) to diagnose and remove systematics present in observed Cosmic Microwave Background (CMB) maps. In this work we focus on the temperature to polarization leakage. We use an iterative QE analysis to remove systematics, in analogy to de-lensing, recovering the primordial B-mode signal and the systematic maps. We introduce a new Gaussian filtering scheme crucial to stable convergence of the iterative cleaning procedure and validate with comparisons to semi-analytical forecasts. We study the limitations of this method by examining its performance both on idealized simulations and on more realistic, non-ideal simulations, where we assume varying de-lensing efficiencies. Finally, we quantify the systematic cleaning efficiency by presenting a likelihood analysis on the tensor to scalar ratio, $r$, and demonstrate that the blind cleaning results in an un-biased measurement of $r$, reducing the systematic induced B-mode power by nearly two orders of magnitude.