Precision cosmology from large-scale structure of the Universe

TL;DR

This paper introduces two tools, CLASS-PT and a theoretical error approach, that enhance the analysis of large-scale structure data for precision cosmology, enabling more accurate parameter estimation without arbitrary scale cuts.

Contribution

The paper presents a new open-source code for one-loop power spectra calculations and a theoretical error method to improve cosmological parameter inference from galaxy clustering data.

Findings

CLASS-PT meets upcoming survey precision standards

The theoretical error approach allows analysis without scale cuts

Optimizes data analysis by accounting for theoretical uncertainties

Abstract

Large scale structure of the Universe becomes a leading source of precision cosmological information. We present two particular tools that can be used in cosmological analyses of the redshift space galaxy clustering data: a new open-source code CLASS-PT and the theoretical error approach. CLASS-PT computes one-loop power auto- and cross-power spectra for matter fields and biased tracers in real and redshift spaces. We show that the code meets the precision standards set by the upcoming high-precision large-scale structure surveys. The theoretical error likelihood approach allows one to analyze galaxy clustering data without having to measure the scale cut $k_{\rm max}$. This approach takes into account that theoretical uncertainties affect parameter estimation gradually, which helps optimize data analysis and ensures that all available cosmological information is extracted.