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The tension in the absolute magnitude of Type Ia supernovae

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arxiv 2307.02434 v1 pith:45VLN4Y3 submitted 2023-07-05 astro-ph.CO

classification astro-ph.CO
keywords tensionassumescosmologicaldiscrepancydistancehubbleindependentmagnitude
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abstract

This study aims to elucidate the tension in the Hubble constant ($H_0$), a key metric in cosmology representing the universe's expansion rate. Conflicting results from independent measurements such as the Planck satellite mission and the SH0ES collaboration have sparked interest in exploring alternative cosmological models. We extend the analysis by SH0ES to an arbitrary cosmographic model, obtaining a competitive local $H_0$ determination which only assumes the standard flat $\Lambda$CDM model ($73.14 \pm 1.10$ km/s/Mpc), and another which only assumes the FLRW metric ($74.56 \pm 1.61$ km/s/Mpc). The study also stresses the importance of the supernova magnitude calibration ($M_B$) in cosmological inference and highlights the tension in $M_B$ when supernovae are calibrated either by CMB and BAO observations or the first two rungs of the cosmic distance ladder. This discrepancy, independent of the physics involved, suggests that models solely changing the Hubble flow and maintaining a sound horizon distance consistent with CMB, fail to explain the discrepancy between early- and late-time measurements of $H_0$.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Skipping the rungs! Calibrating distance indicators through their clustering with galaxies

    astro-ph.CO 2026-07 conditional novelty 6.5 of 10

    Fisher forecasts show angular cross-correlations between distance indicators and galaxy catalogs can constrain a constant calibration offset to ~0.05 mag and H0 to ~1.81 km/s/Mpc with future LSST+DESI data.

  2. A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    A simulation-based inference pipeline (Stjörnumál) fits SN Ia dust and intrinsic scatter models to DES 5-year data, enabling fast Bayesian model comparison across seven SN Ia population models.

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