Bayesian parameter inference is one of the key elements for model selection in cosmological research. However, the inference tools require a large number of calls to simulation codes which can lead to high and sometimes even infeasible computational costs. In this work we combine fast and differentiable emulators with active sampling to accelerate MCMC analyses of CMB physics by 1-2 magnitudes...
Structure formation is a central topic for cosmology. The density perturbation power spectrum, i.e., Gaussian information, has already been constrained by data, but not much is known for the density perturbation bispectrum, the first cumulant beyond pure Gauss. For large-scales, conventional analytical methods based on hydrodynamic approximations provide accurate results, but for smaller...
