Automated calibration of simulated galaxy catalogues for cosmological analyses

TL;DR

This paper introduces an automated pipeline using differential evolution to calibrate galaxy mock catalogues efficiently, improving the accuracy of simulations for cosmological analyses.

Contribution

It presents a novel automated calibration pipeline for galaxy mocks with large parameter spaces, applicable to both standard and modified gravity models.

Findings

Pipeline successfully calibrates galaxy mocks against observations.

Effective for both $ m{ extLambda CDM}$ and modified gravity halo catalogues.

Automates calibration process for large-scale galaxy simulations.

Abstract

Simulated galaxy catalogues have become an essential tool for preparing and exploiting observations from galaxy surveys. They constitute a key ingredient in modelling the systematic uncertainties present in the analysis. However, in order to reach the large volume and high precision required for galaxy surveys, we generally populate dark matter haloes with galaxies following certain theoretical recipes. Such recipes contain free parameters that are calibrated comparing the simulations against observations, but the creation of galaxy mocks is a stochastic process with a large number of free parameters to calibrate. We present a new pipeline, based on the differential evolution algorithm, that can calibrate galaxy mocks in a fully automated way for realistic scenarios with a large parameter space. We apply the pipeline to galaxy mocks built on a combination of halo occupation distribution and sub-halo abundance matching techniques. We show that our pipeline can properly calibrate the galaxy mocks against observations for both $\Lambda$CDM and modified gravity halo catalogues. This type of calibration pipeline provides a new tool for automating the calibration of future massive galaxy mocks.