Power of Observational Hubble Parameter Data: a Figure of Merit Exploration

TL;DR

This study evaluates how simulated Hubble parameter data can constrain cosmological parameters, showing that increasing data quantity and precision enhances their power comparable to supernova data.

Contribution

It introduces a simulated H(z) data model and quantifies the number of measurements needed to match supernova constraints, highlighting the impact of measurement accuracy and priors.

Findings

64 additional H(z) measurements match supernova constraints

Reducing H(z) error to 3% decreases required measurements

Prior knowledge of H_0 improves parameter constraints

Abstract

We use simulated Hubble parameter data in the redshift range 0 \leq z \leq 2 to explore the role and power of observational H(z) data in constraining cosmological parameters of the {\Lambda}CDM model. The error model of the simulated data is empirically constructed from available measurements and scales linearly as z increases. By comparing the median figures of merit calculated from simulated datasets with that of current type Ia supernova data, we find that as many as 64 further independent measurements of H(z) are needed to match the parameter constraining power of SNIa. If the error of H(z) could be lowered to 3%, the same number of future measurements would be needed, but then the redshift coverage would only be required to reach z = 1. We also show that accurate measurements of the Hubble constant H_0 can be used as priors to increase the H(z) data's figure of merit.