Joint Planck and WMAP Assessment of Low CMB Multipoles

TL;DR

This study investigates the suppression of large-scale CMB power by analyzing primordial power spectrum features with Planck and WMAP data, finding models with infra-red cutoffs can explain observed anomalies.

Contribution

It introduces and constrains primordial power spectrum models with features, especially infra-red cutoffs, using combined WMAP and Planck data with model selection criteria.

Findings

Sharp cutoff model fits WMAP data best.

Starobinsky model slightly preferred with combined data.

Infra-red cutoff models can explain large-scale power suppression.

Abstract

The remarkable progress in cosmic microwave background (CMB) studies over past decade has led to the era of precision cosmology in striking agreement with the $\Lambda$CDM model. However, the lack of power in the CMB temperature anisotropies at large angular scales (low-$\ell$), as has been confirmed by the recent Planck data also (up to $\ell=40$), although statistically not very strong (less than $3\sigma$), is still an open problem. One can avoid to seek an explanation for this problem by attributing the lack of power to cosmic variance orcan look for explanations i.e., different inflationary potentials or initial conditions for infl ation to begin with, non-trivial topology, ISW effect etc. Features in the primordial power spectrum (PPS) motivated by the early universe physics has been the most common solution to address this problem. In the present work we also follow this approach and consider a set of PPS which have features and constrain the parameters of those using WMAP 9 year and Planck data employing Markov-Chain Monte Carlo (MCMC) analysis. The prominent feature of all the models of PPS that we consider is an infra-red cut off which leads to suppression of power at large angular scales. We consider models of PPS with maximum three extra parameters and use Akaike information criterion ($AIC$) and Bayesian information criterion ($BIC$) of model and Bayesian information criterion ($BIC$) of model selection to compare the models. For most models, we find good constraints for the cut off scale $k_c$, however, for other parameters our constraints are not that good. We find that sharp cut off model gives best likelihood value for the WMAP 9 year data, but is as good as power law model according to $AIC$. For the joint WMAP 9+Planck data set, Starobinsky model is slightly preferred by $AIC$ which is also able to produce CMB power suppression up to $\ell\leq30$ to some extent.