Figure of Merit and Different Combinations of Observational Data Sets

TL;DR

This paper evaluates the effectiveness of various observational data set combinations in constraining dark energy parameters using the figure of merit, identifying optimal and efficient combinations for cosmological analysis.

Contribution

It introduces a systematic comparison of 11 observational data sets using FoM and identifies the most effective combinations for dark energy constraints.

Findings

Two optimal data set combinations with high FoM.

A simpler combination with comparable constraining power.

Identification of data combinations that balance complexity and effectiveness.

Abstract

To constrain cosmological parameters, one often makes a joint analysis with different combinations of observational data sets. In this paper we take the figure of merit (FoM) for Dark Energy Task Force fiducial model (CPL model) to estimate goodness of different combinations of data sets, which include 11 widely-used observational data sets (Type Ia Supernovae, Observational Hubble Parameter, Baryon Acoustic Oscillation, Cosmic Microwave Background, X-ray Cluster Baryon Mass Fraction, and Gamma-Ray Bursts). We analyze different combinations and make a comparison for two types of combination based on two types of basic combinations, which are often adopted in the literatures. We find two sets of combinations, which have strong ability to constrain the dark energy parameters, one has the largest FoM, the other contains less observational data with a relative large FoM and a simple fitting procedure.