Speaker
Description
Cosmological tensions have recieved much attention in recent years, with the community debating the significance (or lack thereof) of the difference in parameter inferences of $H_0$, $S_8$ or $\Omega_K$ between supernovae, CMB, weak lensing and BAO datasets. Bayesian methods for quantifying tensions across high-dimensional datasets have been developed to robustly determine hidden levels of tension, although these can be expensive to implement in practice due to requirements of nested sampling and MCMC runs across multiple combinations of datasets and models.
This talk will present the current status and products of a DiRAC allocation to construct a next-generation legacy archive: a once-and-for-all coverage and public distribution of nested sampling and MCMC runs across a broad variety of models and dataset combinations, packaged in a zenodo-backed downloadable system unimpeded, as well as preliminary cosmological results. I will also highlight the state-of-the art anesthetic post-processing code, suitable for post-processing chains from MultiNest, PolyChord, dynesty and ultranest, as well as MCMC samplers.
https://github.com/handley-lab/anesthetic
https://github.com/handley-lab/unimpeded
