Coupled Multi Scalar Field Dark Energy

TL;DR

This paper explores multi scalar field models, including interacting quintom models, as candidates for dark energy, demonstrating their potential to better fit cosmological data and cross the phantom divide line.

Contribution

It introduces and constrains complex multi scalar field dark energy models, including interactions, showing their advantages over single-field models in fitting observations.

Findings

Interacting quintom models improve fit to data compared to ΛCDM.

Effective equation of state can cross the phantom divide line.

Multi scalar fields are necessary for consistent dark energy modeling.

Abstract

The main aim of this paper is to present the multi scalar field components as candidates to be the dark energy of the universe and their observational constraints. We start with the canonical Quintessence and Phantom fields with quadratic potentials and show that a more complex model should bear in mind to satisfy current cosmological observations. Then we present some implications for a combination of two fields, named as Quintom models. We consider two types of models, one as the sum of the quintessence and phantom potentials and other including an interacting term between fields. We find that adding one extra degree of freedom, by the interacting term, the dynamics enriches considerably and could lead to an improvement in the fit of $-2\ln\Delta \Like_{\rm max}= 5.19$, compared to $\Lambda$CDM. The resultant effective equation of state is now able to cross the phantom divide line, and in several cases present an oscillatory or discontinuous behavior, depending on the interaction value. The parameter constraints of the scalar field models (quintessence, phantom, quintom and interacting quintom) were performed using Cosmic Chronometers, Supernovae Ia and Baryon Acoustic Oscillations data; and the Log-Bayes factors were computed to compare the performance of the models. We show that single scalar fields may face serious troubles and hence the necessity of a more complex models, i.e. multiple fields.