Dipolar Dark Matter with Massive Bigravity

TL;DR

This paper explores a novel bigravity-based model of dipolar dark matter that reproduces galactic-scale phenomenology like MOND, while addressing theoretical consistency issues such as ghost presence and potential resolutions.

Contribution

It adapts ghost-free massive bigravity to formulate a dipolar dark matter model with promising galactic-scale phenomenology and discusses its theoretical challenges and future directions.

Findings

Successfully reproduces MOND phenomenology at galactic scales

Identifies a ghost in the dark matter sector with potential to be mitigated

Proposes a framework linking bigravity to dark matter dynamics

Abstract

Massive gravity theories have been developed as viable IR modifications of gravity motivated by dark energy and the problem of the cosmological constant. On the other hand, modified gravity and modified dark matter theories were developed with the aim of solving the problems of standard cold dark matter at galactic scales. Here we propose to adapt the framework of ghost-free massive bigravity theories to reformulate the problem of dark matter at galactic scales. We investigate a promising alternative to dark matter called dipolar dark matter (DDM) in which two different species of dark matter are separately coupled to the two metrics of bigravity and are linked together by an internal vector field. We show that this model successfully reproduces the phenomenology of dark matter at galactic scales (MOND) as a result of a mechanism of gravitational polarisation. The model is safe in the gravitational sector, but because of the particular couplings of the matter fields and vector field to the metrics, a ghost in the decoupling limit is present in the dark matter sector. However, it might be possible to push the mass of the ghost beyond the strong coupling scale by an appropriate choice of the parameters of the model. Crucial questions to address in future work are the exact mass of the ghost, and the cosmological implications of the model.