$\texttt{unimpeded}$: A Public Nested Sampling Database for Bayesian Cosmology

TL;DR

The paper introduces 'unimpeded', a Python library and database providing pre-computed nested sampling and MCMC chains for Bayesian cosmology, facilitating model comparison and tension analysis across multiple datasets and models.

Contribution

It offers a publicly accessible repository of pre-computed Bayesian inference chains and tension metrics for various cosmological models and datasets, streamlining complex analyses.

Findings

Provides systematic analysis across 8 cosmological models and 39 datasets.

Enables rapid tension quantification with built-in metrics.

Hosts chains on Zenodo with API access, similar to Planck Archive.

Abstract

Bayesian inference is central to modern cosmology. While parameter estimation is achievable with unnormalised posteriors traditionally obtained via MCMC methods, comprehensive model comparison and tension quantification require Bayesian evidences and normalised posteriors, which remain computationally prohibitive for many researchers. To address this, we present $\texttt{unimpeded}$, a publicly available Python library and data repository providing DiRAC-funded (DP192 and 264) pre-computed nested sampling and MCMC chains with their normalised posterior samples, computed using $\texttt{Cobaya}$ and the Boltzmann solver $\texttt{CAMB}$. $\texttt{unimpeded}$ delivers systematic analysis across a grid of eight cosmological models (including $\Lambda$CDM and seven extensions) and 39 modern cosmological datasets (comprising individual probes and their pairwise combinations). The built-in tension statistics calculator enables rapid computation of six tension quantification metrics. All chains are hosted on Zenodo with permanent access via the unimpeded API, analogous to the renowned Planck Legacy Archive but utilising nested sampling in addition to traditional MCMC methods.