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

Sven Günther, Julien Lesgourgues, Georgios Samaras, Nils Schöneberg, Florian Stadtmann, Christian Fidler and Jesus Torrado


In modern analysis pipelines, Einstein-Boltzmann Solvers (EBSs) are an invaluable tool for obtaining CMB and matter power spectra. To significantly accelerate the
computation of these observables, the CosmicNet strategy is to replace the usual bottleneck
of an EBS, which is the integration of a system of differential equations for linear cosmological
perturbations, by trained neural networks. This strategy offers several advantages compared
to the direct emulation of the final observables, including very small networks that are easy
to train in high-dimensional parameter spaces, and which do not depend by construction
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 ΛCDM, with massive neutrinos, extra
relativistic degrees of freedom, spatial curvature, and dynamical dark energy. We demonstrate the accuracy and performance of CosmicNet by presenting several parameter inference runs from Planck, BAO and supernovae data, performed with classnet and the cobaya inference package. We obtain a speedup factor of order 150 for the emulated perturbation module of class. For the whole code, this translates into an overall speedup factor of order 3 when computing CMB harmonic spectra, and of order 50 when computing matter power spectra.