Accelerated expansion from ghost-free bigravity: a statistical analysis with improved generality
Yashar Akrami, Tomi S. Koivisto, Marit Sandstad (Inst. Theor., Astrophys., Oslo U.)

TL;DR
This paper performs a comprehensive statistical analysis of ghost-free bigravity cosmology, showing it can mimic Lambda-CDM and fit observational data well, but with parameter degeneracies and no clear statistical preference.
Contribution
It provides the first extensive Bayesian and frequentist analysis of bigravity models against cosmological data, assessing their viability as alternatives to Lambda-CDM.
Findings
Bigravity models can produce late-time acceleration similar to Lambda-CDM.
The models fit observational data well at the background level.
Parameters of the bigravity model are highly degenerate, requiring further analysis.
Abstract
We study the background cosmology of the ghost-free, bimetric theory of gravity. We perform an extensive statistical analysis of the model using both frequentist and Bayesian frameworks and employ the constraints on the expansion history of the Universe from the observations of supernovae, the cosmic microwave background and the large scale structure to estimate the model's parameters and test the goodness of the fits. We explore the parameter space of the model with nested sampling to find the best-fit chi-square, obtain the Bayesian evidence, and compute the marginalized posteriors and mean likelihoods. We mainly focus on a class of sub-models with no explicit cosmological constant (or vacuum energy) term to assess the ability of the theory to dynamically cause a late-time accelerated expansion. The model behaves as standard gravity without a cosmological constant at early times, with…
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.
