Non-parametric exploration of minimally coupled gravity with phantom crossing

TL;DR

This paper investigates minimally coupled Horndeski gravity models, specifically kinetic gravity braiding, to see if they can explain dark energy behaviors like phantom crossing, using non-parametric methods and current observational data.

Contribution

It performs a non-parametric exploration of kinetically coupled Horndeski models, showing that phantom crossing can occur without conformal coupling and remain consistent with observations.

Findings

Viable phantom-crossing models exist within minimally coupled Horndeski gravity.

Models are consistent with CMB, large-scale structure, and galaxy-CMB cross-correlations.

Supports future Bayesian analyses with non-parametric EFT priors.

Abstract

Recent measurements of the baryon acoustic oscillations by the Dark Energy Spectroscopic Instrument (DESI), especially when combined with cosmic microwave background (CMB) and supernova data, favor a late-time dark energy equation of state that crosses $w=-1$, which has been argued to point toward non-minimal conformal coupling in Horndeski gravity. We test this interpretation by performing a non-parametric exploration of the minimally coupled, luminal Horndeski subclass known as kinetic gravity braiding (KGB). Using mochi_class and its manifestly stable effective field theory (EFT) basis implementation, we efficiently scan a broad class of models in which the EFT functions are allowed to vary freely in time, while enforcing the absence of ghost and gradient instabilities from the outset. We identify a set of KGB models that realize phantom crossing and remain broadly consistent with current probes of the background and linear large-scale structure, including CMB temperature and lensing power spectra, redshift-space distortions, cosmic shear, and the cross-correlation between galaxies and the Integrated Sachs-Wolfe effect. Our results demonstrate that viable phantom-crossing solutions exist without conformal coupling, motivating future full Bayesian analyses of this model class with non-parametric EFT priors.