Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory

Understand

The linear matter power spectrum is an essential ingredient in all theoretical models for interpreting large-scale-structure observables.

• Although Boltzmann codes such as CLASS or CAMB are very efficient at computing the linear spectrum, the analysis of data usually requires $10^4$-$10^6$ evaluations, which means this task can be the most computationally expensive aspect of data analysis.
• Here, we address this problem by building a neural network emulator that provides the linear theory (total and cold) matter power spectrum in about one millisecond with 0.2% (0.5%) accuracy over redshifts $z \le 3$ ($z \le 9$), and scales $10^{-4} \le k \, [h {\rm Mpc^{-1}}] < 50$.
• We train this emulator with more than 200,000 measurements, spanning a broad cosmological parameter space that includes massive neutrinos and dynamical dark energy.