80 research outputs found
A Robust Bayesian Meta-Analysis for Estimating the Hubble Constant via Time Delay Cosmography
We propose a Bayesian meta-analysis to infer the current expansion rate of
the Universe, called the Hubble constant (), via time delay cosmography.
Inputs of the meta-analysis are estimates of two properties for each pair of
gravitationally lensed images; time delay and Fermat potential difference
estimates with their standard errors. A meta-analysis can be appealing in
practice because obtaining each estimate from even a single lens system
involves substantial human efforts, and thus estimates are often separately
obtained and published. This work focuses on combining these estimates from
independent studies to infer in a robust manner. For this purpose, we
adopt Student's error for the inputs of the meta-analysis. We investigate
properties of the resulting estimate via two simulation studies with
realistic imaging data. It turns out that the meta-analysis can infer
with sub-percent bias and about 1 percent level of coefficient of variation,
even when 30 percent of inputs are manipulated to be outliers. We also apply
the meta-analysis to three gravitationally lensed systems, and estimate
by (km/second/Mpc), which covers a wide range of
estimates obtained under different physical processes. An R package, h0, is
publicly available for fitting the proposed meta-analysis
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