Systematic assessment of the Hubble tension via Bayesian jackknife testing

TL;DR

This paper uses a Bayesian jackknife approach to analyze 16 independent measurements of the Hubble constant, aiming to identify measurement clusters and assess potential biases to better understand the Hubble tension.

Contribution

It introduces a systematic Bayesian jackknife method to evaluate measurement clusters and biases in Hubble constant data, providing a model-agnostic posterior estimate.

Findings

No measurement subset is definitively unbiased.

The model where all measurements are biased is strongly disfavoured.

The estimated Hubble constant is around 68 km/s/Mpc with a 95 ext{% credible interval of 66.7 to 72.7 km/s/Mpc.

Abstract

Statistically-significant differences in the value of the Hubble parameter are found depending on the measurement method that is used, a result known as the Hubble tension. A variety of ways of comparing, grouping, and excluding measurements have been used to try to explain this, either in terms of physical effects or systematic errors. We present a systematic 'Bayesian jackknife' analysis of 16 independent measurements of the Hubble parameter in an attempt to identify whether the measurements fall into meaningful clusters that would help explain the origin of the tension. After evaluating evidence ratios for the commonly-used split into early- vs late-time measurements, we then study a range of simplified alternative physical scenarios that reflect different physical origins of an apparent bias or shift in the value of $H_0$, assigning phenomenological population parameters to each subset. These include scenarios where specific subsets are biased (e.g. due to unrecognised experimental systematics in the local distance ladder or cosmic microwave background measurements), as well as more cosmologically-motivated cases involving modifications to the expansion history. Many of these scenarios have similar marginal likelihood, but the model where no measurements are biased is strongly disfavoured. Finally, we marginalise over all these scenarios to estimate the 'model agnostic' posterior distribution of $H_0$. The resulting distribution is mildly multi-modal, but modestly favours values near $H_0=68$ km/s/Mpc, with a 95\% credible region of $66.7 < H_0 < 72.7$ km/s/Mpc.