标题： 观测哈勃参数数据的力量：优值指数探索 显示英文标题

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

摘要： 我们使用红移范围在 0 \leq z \leq 2 的模拟哈勃参数数据，来探讨观测 H(z) 数据在限制 {\Lambda }CDM 模型宇宙学参数方面的角色和能力。 模拟数据的误差模型基于可用测量值经验构建，并且随着 z 增大线性增长。 通过比较从模拟数据集中计算出的中位数品质因子与当前Ia型超新星数据的品质因子，我们发现需要多达64个进一步独立的H(z)测量值才能匹配Ia型超新星的数据限制能力。 如果H(z)的误差能够降低到3%，则同样数量的未来测量值就足够了，但此时红移覆盖范围只需达到 z = 1 即可。 我们还表明，精确测量哈勃常数 H_0 可以作为先验信息来提高 H(z) 数据的品质因子。 显示英文摘要

摘要： 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.