Effort: a fast and differentiable emulator for the Effective Field Theory of the Large Scale Structure of the Universe

TL;DR

Effort is a fast, accurate, and differentiable emulator for the Effective Field Theory of Large-Scale Structure, enabling efficient analysis of cosmological data with validated reliability.

Contribution

The paper introduces Effort, a novel emulator that combines advanced numerical methods and preprocessing for efficient, accurate EFTofLSS analysis, validated against existing tools.

Findings

Effort achieves high computational efficiency without losing accuracy.

Bayesian posteriors from Effort agree with those from pybird within Monte Carlo noise.

Effort is suitable for analyzing large cosmological datasets and future surveys.

Abstract

We present the official release of the EFfective Field theORy surrogaTe (Effort), a novel and efficient emulator designed for the Effective Field Theory of Large-Scale Structure (EFTofLSS). This tool combines state-of-the-art numerical methods and clever preprocessing strategies to achieve exceptional computational performance without sacrificing accuracy. To validate the emulator reliability, we compare Bayesian posteriors sampled using Effort via Hamiltonian MonteCarlo methods to the ones sampled using the widely-used pybird code, via the Metropolis-Hastings sampler. On a large-volume set of simulations, and on the BOSS dataset, the comparison confirms excellent agreement, with deviations compatible with MonteCarlo noise. Looking ahead, Effort is poised to analyze next-generation cosmological datasets and to support joint analyses with complementary tools.