# Cosmological parameter results

## Description

The cosmological parameter results explore a variety of cosmological models with combinations of Planck and other data. We provide results from MCMC exploration chains, as well as best fits, and sets of parameter tables. Definitions, conventions and reference are contained in #planck2013-p11.

## Production process

Parameter chains are produced using CosmoMC, a sampling package available from [1]. This includes the sample analysis package GetDist, and the scripts for managing and analysing the full grid or runs. Chain products provided here have had burn in removed. Some results with additional data are produced by importance sampling.

## Caveats and known issues

Confidence intervals are derived from the MCMC samples, and assume the input likelihoods are exactly correct, so there is no quantification for systematic errors other than via the covariance, foreground and beam error models assumed in the likelihood codes. We had some issues producing reliable results from the minimizer used to produce the best fits, so in some cases the quoted fits may be significantly improved.

## Related products

Results of the parameter exploration runs should be reproducible using CosmoMC with the Planck likelihood code.

## Parameter Tables

These list paramter constraints for each considered model and data combination separately

There are also summary comparison tables, showing how constraints for selected models vary with data used to constrain them:

## Parameter Chains

We provide the full chains and getdist outputs for our parameter results.

The download contains a hierarchy of directories, with each separate chain in a separate directory. The structure for the directories is

base_AAA_BBB/XXX_YYY_.../


where AAA and BBB are any additional parameters that are variend in addition to the six parameters of the baseline model. XXX, YYY, etc encode the data combinations used. The parameter tags are defined in #planck2013-p11. Data combination tags are as follows (see the parameters paper for full description and references):

Data tags
Tag Data
planck high-L Planck temperature (CamSpec, 50 <= l <= 2500)
lowl low-L: Planck temperature (2 <= l <= 49)
lensing Planck lensing power spectrum reconstruction
lowLike low-L WMAP 9 polarization
tauprior A Gaussian prior on the optical depth, tau = 0.09 +- 0.013
BAO Baryon oscillation data from DR7, DR9 and and 6DF
SNLS Supernova data from the Supernova Legacy Survey
Union2 Supernova data from the Union compilation
HST Hubble parameter constraint from HST (Riess et al)
WMAP The full WMAP (temperature and polarization) 9 year data

Data likelihoods are either included when running the chains, or by importance sampling. Data combinations that are added by importance sampling appear at the end of the list, following the {\tt post{\textunderscore}} tag. WMAP9 chains are run from the WMAP9 likelihood code with the same baseline assumptions as \textit{Planck}, and hence may different slightly from those available on Lambda (e.g. the baseline model has non-zero neutrino mass). Note that the best fits are merely examples of parameter combinations that fit the data well, due to parameter degeneracies there may be other combinations of parameters that fit the data nearly equally well.

Beneath each table is the minus log Likelihood $\chi^2_{\rm eff}$ for each best fit model, and also the contributions coming from each separate part of the likelihood. The $R-1$ value is also given, which measures the convergence of the sampling chains, with small values being better converged. The sampling uncertainty on quoted mean values are typically of order $R-1$ in units of the standard deviation.

## References

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1. References

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