Iterative map-making for scanning experiments

TL;DR

This paper introduces an iterative approach to simultaneously estimate noise characteristics and produce maximum-likelihood maps from scanning experiment data, improving data analysis for experiments like CMB observations.

Contribution

The paper presents a novel iterative method that jointly estimates noise power spectrum and maps, enhancing robustness in data analysis for scanning experiments.

Findings

Method successfully recovers noise power spectrum from simulated data.

Produces accurate maximum-likelihood maps in the presence of colored noise.

Demonstrates robustness on simulated datasets resembling CMB experiment data.

Abstract

We describe here an iterative method for jointly estimating the noise power spectrum from a scanning experiment's time-ordered data, together with the maximum-likelihood map. We test the robustness of this method on simulated datasets with colored noise, like those of bolometer receivers in CMB experiments.