Model independent approaches to reionization in the analysis of upcoming CMB data

TL;DR

This paper evaluates two model-independent methods for analyzing reionization in upcoming CMB data, demonstrating their effectiveness in avoiding biases in key cosmological parameters using simulated Planck data.

Contribution

It compares two recent reionization fitting methods through Monte Carlo analysis, showing their advantages and impact on parameter estimation accuracy.

Findings

Wrong reionization assumptions bias , A_s, r by 1-3 standard deviations

Model-independent methods do not significantly degrade main parameter estimates

Neglecting Helium increases biases in , r, A_s even with general reionization models

Abstract

On large angular scales, CMB polarization depends mostly on the evolution of the ionization level of the IGM during reionization. In order to avoid biasing parameter estimates, an accurate and model independent approach to reionization is needed when analyzing high precision data, like those expected from the Planck experiment. In this paper we consider two recently proposed methods of fitting for reionization and we discuss their respective advantages. We test both methods by performing a MonteCarlo Markov Chain analysis of simulated Planck data, assuming different fiducial reionization histories. We take into account both temperature and polarization data up to high multipoles, and we fit for both reionization and non reionization parameters. We find that while a wrong assumption on reionization may bias \tau_e, A_s and r by 1-3 standard deviations, other parameters, in particular n_s, are not significantly biased. The additional reionization parameters introduced by considering the model independent methods do not affect the accuracy of the estimates of the main cosmological parameters, the biggest degradation being of order ~ 15% for \tau_e. Finally, we show that neglecting Helium contribution in the analysis increases the bias on \tau_e, r and A_s even when a general fitting approach to reionization is assumed.