CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks

Understand

In modern analysis pipelines, Einstein-Boltzmann Solvers (EBSs) are an invaluable tool for obtaining CMB and matter power spectra.

• To accelerate the computation of these observables, the CosmicNet strategy is to replace the bottleneck of an EBS, which is the integration of a system of differential equations for linear cosmological perturbations, by neural networks.
• This strategy offers advantages compared to the direct emulation of the final observables, including small networks that are easy to train in high-dimensional parameter spaces, and which do not depend by on primordial spectrum parameters nor observation-related quantities such as selection functions.
• In this second CosmicNet paper, we present a more efficient set of networks that are already trained for extended cosmologies beyond LCDM, with massive neutrinos, extra relativistic degrees of freedom, spatial curvature, and dynamical dark energy.