Weyl geometry is equivalent to Riemannian geometry of a non-local dressed metric g*_{\mu\nu} via Wilson lines, with the quadratic and WDBI actions taking the same form in the symmetric phase.
Higgs-Dilaton Cosmology: From the Early to the Late Universe
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abstract
We consider a minimal scale-invariant extension of the Standard Model of particle physics combined with Unimodular Gravity formulated in \cite{Shaposhnikov:2008xb}. This theory is able to describe not only an inflationary stage, related to the Standard Model Higgs field, but also a late period of Dark Energy domination, associated with an almost massless dilaton. A number of parameters can be fixed by inflationary physics, allowing to make specific predictions for any subsequent period. In particular, we derive a relation between the tilt of the primordial spectrum of scalar fluctuations, $n_s$, and the present value of the equation of state parameter of dark energy, $\omega_{DE}^0$. We find bounds for the scalar tilt, $n_s<0.97$, the associated running, $-0.0006<d\ln n_s/d\ln k\lesssim-0.00015$, and for the scalar-to-tensor ratio, $0.0009\lesssim r<0.0033$, which will be critically tested by the results of the Planck mission. For the equation of state of dark energy, the model predicts $\omega_{DE}^0>-1$. The relation between $n_s$ and $\omega_{DE}^0$ allows us to use the current observational bounds on $n_s$ to further constrain the dark energy equation of state to $0< 1+\omega_{DE}^0< 0.02$, which is to be confronted with future dark energy surveys.
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Reexamination of the SO(16)xSO(16)' nonsupersymmetric model for implications on dark energy, vacuum stabilization, dark matter candidates, and gauge-Higgs unification in light of LHC and dark energy data.
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Dark Horse, Dark Matter: Revisiting the SO(16)x SO(16)' Nonsupersymmetric Model in the LHC and Dark Energy Era
Reexamination of the SO(16)xSO(16)' nonsupersymmetric model for implications on dark energy, vacuum stabilization, dark matter candidates, and gauge-Higgs unification in light of LHC and dark energy data.