cosmology

Constraints on Horndeski Gravity with Phantom Crossing

arXiv:2606.20794

summary

The paper introduces Asymptotic Cubic Galileon (ACG) models, a subclass of Horndeski scalar‑tensor theories that can cross the phantom divide (w = –1) while remaining minimally coupled to matter, and tests them against CMB, BAO, supernova, galaxy‑ISW, and void observations, finding they fit data as well as phenomenological w0waCDM models.

Abstract

Gravity models in which the dark energy equation of state crosses , also known as the phantom divide, have received extensive interest due to recent analyses favouring this behaviour. We introduce a new subclass of Horndeski scalar-tensor models capable of generating phantom crossing, whilst remaining minimally coupled to matter: the Asymptotic Cubic Galileon (ACG) models. We show that ACG models can jointly fit the expansion history inferred from observations of the Planck cosmic microwave background, baryon acoustic oscillation measurements from the Dark Energy Spectroscopic Instrument, and distance-ladder supernovae measurements from the Dark Energy Survey. We then demonstrate that perturbative observables, including the galaxy-ISW cross-correlation and void force profile, provide powerful constraints that confine viable and testable ACG models to a well-defined region of the broader Horndeski landscape. Model comparison metrics, including and Bayesian evidence, favour both ACG and CDM models over CDM, with ACG providing a fit of comparable quality to CDM. Crucially, ACG models ground the observationally preferred CDM behaviour in a robust Lagrangian formulation. This enables interpretation beyond mere phenomenological fits, and motivates further tests of these models on nonlinear scales.

21 pages, 11 figures. Minor revisions incorporating feedback received by email. Submitted to MNRAS

Topics & keywords

#horndeski gravity#scalar‑tensor theories#phantom crossing#dark energy#observational constraintsAsymptotic Cubic Galileonphantom dividew = -1 crossingCMBBAOISW cross‑correlationBayesian evidence