Framework for performance forecasting and optimization of CMB B-mode observations in presence of astrophysical foregrounds

TL;DR

This paper introduces a formalism for forecasting and optimizing CMB B-mode experiments, accounting for foregrounds and noise, to improve design choices and scientific outcomes.

Contribution

It presents a new formalism for optimizing CMB B-mode experiments considering foreground subtraction and instrumental configurations.

Findings

Optimization can enhance scientific results by a factor of a few.

Design simplification is possible without losing science goals.

The method guides robust experimental design even with uncertain foreground models.

Abstract

We present a formalism for performance forecasting and optimization of future cosmic microwave background (CMB) experiments. We implement it in the context of nearly full sky, multifrequency, B-mode polarization observations, incorporating statistical uncertainties due to the CMB sky statistics, instrumental noise, as well as the presence of the foreground signals. We model the effects of a subtraction of these using a parametric maximum likelihood technique and optimize the instrumental configuration with predefined or arbitrary observational frequency channels, constraining either a total number of detectors or a focal plane area. We showcase the proposed formalism by applying it to two cases of experimental setups based on the CMBpol and COrE mission concepts looked at as dedicated B-mode experiments. We find that, if the models of the foregrounds available at the time of the optimization are sufficiently precise, the procedure can help to either improve the potential scientific outcome of the experiment by a factor of a few, while allowing one to avoid excessive hardware complexity, or simplify the instrument design without compromising its science goals. However, our analysis also shows that even if the available foreground models are not considered to be sufficiently reliable, the proposed procedure can guide a design of more robust experimental setups. While better suited to cope with a plausible, greater complexity of the foregrounds than that foreseen by the models, these setups could ensure science results close to the best achievable, should the models be found to be correct.