The emulator enhances accuracy and performance in analyzing large scale structure, suggesting robust applications with Bayesian posteriors.
We present the official release of the EFfective Field theORy surrogaTe ( Effort.jl ), 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.jl 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.jl is poised to analyze next-generation cosmological datasets and to support joint analyses with complementary tools.
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Bonici et al. (2025) studied this question.
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