Non-Gaussian Likelihoods for Type Ia Supernovae Cosmology: Implications for Dark Energy and $H_0$
Toby Lovick, Suhail Dhawan, Will Handley

TL;DR
This paper investigates the impact of non-Gaussian likelihoods on Type Ia supernovae cosmology, showing that improved scatter modeling refines dark energy constraints and reduces Hubble constant uncertainty, highlighting the importance of statistical assumptions.
Contribution
It introduces a Bayesian framework incorporating non-Gaussian scatter models for supernova data, improving cosmological parameter estimation and Hubble constant measurement.
Findings
Flat ΛCDM is preferred over other dark energy models
Non-Gaussian scatter models improve data fit and increase Bayesian evidence
Refined analysis reduces H0 uncertainty and confirms tension with Planck
Abstract
The latest improvements in the scale and calibration of Type Ia supernovae catalogues allow us to constrain the specific nature and evolution of dark energy through its effect on the expansion history of the universe. We present the results of Bayesian cosmological model comparison on the SNe~Ia catalogue Pantheon+, where Flat CDM is preferred by the data over all other models and we find moderate evidence () to reject a number of the alternate dark energy models. The effect of peculiar velocity corrections on model comparison is analysed, where we show that removing the peculiar velocity corrections results in a varying fit on non-CDM parameters. As well as comparing cosmological models, the Bayesian methodology is extended to comparing the scatter model of the data, testing for non-gaussianity in the Pantheon+ Hubble residuals. We…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGamma-ray bursts and supernovae · Astronomy and Astrophysical Research
