Constraints on Lemaître-Tolman-Bondi models from Observational Hubble Parameter data

Details Constraints on Lemaître-Tolman-Bondi models from Observational Hubble Parameter data Constraints on Lemaître-Tolman-Bondi models from Observational Hubble Parameter data

2011-11-10

arxiv.org/abs/1111.2400v2

Astrophys.J. 748 (2012) 111

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

14 pages, 14 figures, 5 tables, accepted for publication in ApJ

Scientific paper

10.1088/0004-637X/748/2/111

We use the observational Hubble parameter data (OHD), both the latest observational dataset (Stern et al. 2010, referred to as SJVKS) and the simulated datasets, to constrain Lema\^{\i}tre-Tolman-Bondi (LTB) void models. The necessity of the consistency check on OHD itself in the LTB cosmology is stressed. Three voids are chosen as test models and are constrained using the Union2 dataset of SN Ia as well as OHD. Despite their different parametrization, the results from our test models show some indicating similarities, e.g., the best-fit voids obtained from OHD are all considerably broader than those from SN Ia. Due to the small size of the SJVKS dataset, the constraints are not conclusive. The constraining power of the future OHD observations are therefore investigated, through a Figure of Merit (FoM) analysis based on the Monte Carlo simulated data. We found that, in the case that the future OHD become more consistent with SN Ia, the results from the test models are almost unanimous: 1) as many as 32 OHD data points at the SJVKS-like uncertainty level are needed to give a higher FoM than the Union2 dataset of SN Ia; 2) precise observation helps reduce this required number; 3) increasing the survey depth does not always increase the FoM. On the other hand, if the future OHD and the Union2 dataset keep favor different voids, in a similar manner as they do at present, the 1{\sigma} confidence regions obtained from the two probes should finally separate. We test this conjecture and found that, the minimum observational requirement (the size of the dataset, the uncertainty level and the survey depth) for this inconsistency to emerge depends strongly on the void model.

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