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Prospects for resolving the Hubble constant tension with standard sirens

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 Added by Stephen Feeney
 Publication date 2018
  fields Physics
and research's language is English




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The Hubble constant ($H_0$) estimated from the local Cepheid-supernova (SN) distance ladder is in 3-$sigma$ tension with the value extrapolated from cosmic microwave background (CMB) data assuming the standard cosmological model. Whether this tension represents new physics or systematic effects is the subject of intense debate. Here, we investigate how new, independent $H_0$ estimates can arbitrate this tension, assessing whether the measurements are consistent with being derived from the same model using the posterior predictive distribution (PPD). We show that, with existing data, the inverse distance ladder formed from BOSS baryon acoustic oscillation measurements and the Pantheon SN sample yields an $H_0$ posterior near-identical to the Planck CMB measurement. The observed local distance ladder value is a very unlikely draw from the resulting PPD. Turning to the future, we find that a sample of $sim50$ binary neutron star standard sirens (detectable within the next decade) will be able to adjudicate between the local and CMB estimates.



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Multi-messenger observations of binary neutron star mergers offer a promising path towards resolution of the Hubble constant ($H_0$) tension, provided their constraints are shown to be free from systematics such as the Malmquist bias. In the traditional Bayesian framework, accounting for selection effects in the likelihood requires calculation of the expected number (or fraction) of detections as a function of the parameters describing the population and cosmology; a potentially costly and/or inaccurate process. This calculation can, however, be bypassed completely by performing the inference in a framework in which the likelihood is never explicitly calculated, but instead fit using forward simulations of the data, which naturally include the selection. This is Likelihood-Free Inference (LFI). Here, we use density-estimation LFI, coupled to neural-network-based data compression, to infer $H_0$ from mock catalogues of binary neutron star mergers, given noisy redshift, distance and peculiar velocity estimates for each object. We demonstrate that LFI yields statistically unbiased estimates of $H_0$ in the presence of selection effects, with precision matching that of sampling the full Bayesian hierarchical model. Marginalizing over the bias increases the $H_0$ uncertainty by only $6%$ for training sets consisting of $O(10^4)$ populations. The resulting LFI framework is applicable to population-level inference problems with selection effects across astrophysics.
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