Abstract
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. We release a new branch of the CLASS code, called
CLASSNET, which automatically uses networks within a region of trusted
accuracy. We demonstrate the accuracy and performance of CLASSNET by presenting
parameter inference runs from Planck, BAO and supernovae data, performed with
CLASSNET and the COBAYA inference package. We have eliminated the perturbation
module as a bottleneck of the EBS, with a speedup that is even more remarkable
in extended cosmologies, where the usual approach would have been more
expensive while the network's performance remains the same. 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 (now dominated by the highly parallelizable and
further optimizable line-of-sight integration), and of order 50 when computing
matter power spectra (less than 0.1 seconds even in extended cosmologies).
Description
CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks
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