Neural Network Acceleration of Large-scale Structure Theory Calculations

Understand

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.