Cosmic Microwave Background and the issue of a fundamental preferred frame

TL;DR

This paper explores the potential link between ether-drift measurements, CMB observations, and a fundamental preferred frame, suggesting that vacuum properties and temperature gradients could explain observed anisotropies.

Contribution

It proposes a novel interpretation of vacuum anisotropies as evidence for a preferred frame, connecting CMB data with ether-drift experiments and vacuum refractivity.

Findings

Vacuum anisotropy could be explained by differences in vacuum refractivity.

Gaseous matter enhances the ether-drift signal via temperature gradients.

Irregular signals may reflect the stochastic nature of the vacuum and CMB dipole.

Abstract

Correlating ether-drift measurements in laboratory and CMB observations in space would confirm the existence of a preferred reference frame. To this end, however, the velocity of light in the interferometers cannot be the same parameter 'c' of Lorentz transformations. Thus, for the earth velocity of 370 km/s, a fundamental 10^(-15) light anisotropy, as presently observed in vacuum and in solid dielectrics, could reveal a 10^(-9) difference in the vacuum refractivity between an ideal freely-falling frame and an apparatus on the earth surface. In this perspective, the stochastic nature of the vacuum could explain the irregular character of the signal and the substantial reduction of its statistical average (10^(-18) or smaller). For the same v=370 km/s the different refractivity, about 10^(-4) and 10^(-5) for air or helium at atmospheric pressure, could also explain the observed anisotropy, respectively about 10^(-10) and 10^(-11). The mechanism enhancing the signal in gaseous matter, but ineffective in solid dielectrics, is naturally identified in a non-local, temperature gradient of a fraction of millikelvin. This is found in all classical experiments and might ultimately reflect the CMB temperature dipole or the fundamental energy flow in a Lorentz-non-invariant vacuum. Clarification requires dedicated experiments and improvements in the data analysis.