Application of Bayesian model averaging to measurements of the primordial power spectrum

TL;DR

This paper applies Bayesian model averaging to cosmological data to better estimate uncertainties in the primordial power spectrum parameters, accounting for model uncertainty.

Contribution

It introduces the use of Bayesian model averaging for primordial power spectrum parameter estimation using cosmic microwave and large-scale structure data.

Findings

Model-averaged 95% credible interval for spectral index: 0.940 < n_s < 1.000

Model averaging tightens the credible upper limit for tensor models

Results incorporate data from WMAP, ACBAR, BOOMERanG, CBI, and SDSS DR7

Abstract

Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model. Bayesian model averaging is a method for assessing parameter uncertainties in situations where there is also uncertainty in the underlying model. We apply model averaging to the estimation of the parameters associated with the primordial power spectra of curvature and tensor perturbations. We use CosmoNest and MultiNest to compute the model Evidences and posteriors, using cosmic microwave data from WMAP, ACBAR, BOOMERanG and CBI, plus large-scale structure data from the SDSS DR7. We find that the model-averaged 95% credible interval for the spectral index using all of the data is 0.940 < n_s < 1.000, where n_s is specified at a pivot scale 0.015 Mpc^{-1}. For the tensors model averaging can tighten the credible upper limit, depending on prior assumptions.