Neural Network Acceleration of Large-scale Structure Theory Calculations
  We make use of neural networks to accelerate the calculation of power spectra
required for the analysis of galaxy clustering and weak gravitational lensing
data. For modern perturbation theory codes, evaluation time for a single
cosmology and redshift can take on the order of two seconds. In combination
with the comparable time required to compute linear predictions using a
Boltzmann solver, these calculations are the bottleneck for many contemporary
large-scale structure analyses. In this work, we construct neural network-based
surrogate models for Lagrangian perturbation theory (LPT) predictions of matter
power spectra, real and redshift space galaxy power spectra, and galaxy--matter
cross power spectra that attain $\sim 0.1\%$ (at one sigma) accuracy over a
broad range of scales in a $w$CDM parameter space. The neural network
surrogates can be evaluated in approximately one millisecond, a factor of 1000
times faster than the full Boltzmann code and LPT computations. In a simulated
full-shape redshift space galaxy power spectrum analysis, we demonstrate that
the posteriors obtained using our surrogates are accurate compared to those
obtained using the full LPT model. We make our surrogate models public at
https://github.com/sfschen/EmulateLSS, so that others may take advantage of the
speed gains they provide to enable rapid iteration on analysis settings,
something that is essential in complex contemporary large-scale structure
analyses.
