Emulating cosmological growth functions with B-Splines

TL;DR

This paper introduces B-spline emulators to efficiently approximate cosmological growth functions, significantly reducing computation time in simulations while maintaining accuracy for inference tasks.

Contribution

It presents a novel method for constructing conditional B-spline emulators for growth functions, enabling faster cosmological simulations without biasing results.

Findings

Over an order of magnitude speedup in simulations

Emulators accurately reproduce growth functions

No bias introduced in cosmological inference

Abstract

In the light of GPU accelerations, sequential operations such as solving ordinary differential equations can be bottlenecks for gradient evaluations and hinder potential speed gains. In this work, we focus on growth functions and their time derivatives in cosmological particle mesh simulations and show that these are the majority time cost when using gradient based inference algorithms. We propose to construct novel conditional B-spline emulators which directly learn an interpolating function for the growth factor as a function of time, conditioned on the cosmology. We demonstrate that these emulators are sufficiently accurate to not bias our results for cosmological inference and can lead to over an order of magnitude gains in time, especially for small to intermediate size simulations.