Distance-Ladder Measurements of the Hubble Constant: Recent Progress, Systematics, and Prospects

arXiv:2606.26831 · astro-ph.CO, astro-ph.SR · Submitted 2026-06-25 · Read on arXiv

Xiaodian Chen, Shu Wang

astro-ph.CO, astro-ph.SR

Submitted: 2026-06-25

Comments: Review article; accepted for publication in Research in Astronomy and Astrophysics; 38 pages, 6 figures; comments welcome

License: http://creativecommons.org/licenses/by/4.0/

The gist: The Hubble constant, H 0, links the nearby distance scale to the present cosmic expansion rate.

Terminology

Abstract

The Hubble constant, H 0, links the nearby distance scale to the present cosmic expansion rate. Local distance-ladder measurements now reach percent-level precision and remain more than 5 sigma higher than the value inferred from cosmic microwave background (CMB) observations in base- CDM, making the reliability of the local ladder a central issue in the Hubble tension. We describe the ladder as a covariance network connecting level-0 geometric anchors, level-1 stellar distance indicators, and level-2 Hubble-flow probes. The Cepheid--Type Ia supernova (SN Ia) route remains the most precise single local ladder, but independent indicators including the tip of the red giant branch (TRGB), J-region asymptotic giant branch (JAGB) stars, Mira variables, surface-brightness fluctuations (SBF), the Tully--Fisher relation, and Type II supernovae (SNe II) now test shared and method-specific systematics. In a compact seven-route covariance summary, combining the Cepheid--SN Ia route with three level-1 alternatives (TRGB, JAGB, and Mira) and three level-2 alternatives (SBF, Tully--Fisher, and SNe II) gives H 0=73.30 plus or minus0.92 km s-1 Mpc-1, still 5.6 sigma above Planck base- CDM. JWST has already tested Cepheid crowding and is making independent TRGB-based H 0 measurements increasingly feasible. Over the next five years, a reliable one-percent local H 0 requires larger calibrator samples, cross-validated level-1 zero points, explicit covariance propagation, and AI-assisted, reproducible, pre-specified selection criteria for distance-indicator measurements.

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