Abstract

When a supercooled liquid approaches glass transition, viscous flow slows down greatly, but often the Brownian motion of a molecular probe in the host liquid does not slow down as much, causing the Stokes-Einstein relation to fail by orders of magnitude. Here we formulate a theory that relates the Brownian motion of the probe to two concurrent processes in the host liquid: viscous flow and molecular hopping. Molecular hopping prevails over viscous flow when the probe is small and the temperature is low. Our theory generalizes the Stokes-Einstein relation and fits the experimental data remarkably well.

Highlights

  • The cosmic microwave background (CMB) [1] is an essential source of information about all epochs of the Universe

  • An extension to the standard big bang model, inflation, postulates a short period of exponential expansion in the very early Universe, naturally setting the initial conditions required by ΛCDM, as well as solving a number of additional problems in standard cosmology

  • The amplitude of tensors is conventionally parametrized by r, the tensor-to-scalar ratio at a fiducial scale

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Summary

INTRODUCTION

The cosmic microwave background (CMB) [1] is an essential source of information about all epochs of the Universe. Tensor modes produce a small increment in the temperature anisotropy power spectrum over the standard ΛCDM scalar perturbations at multipoles l ≲ 60; measuring this increment requires the large sky coverage traditionally achieved by space-based experiments, and an understanding of the other cosmological parameters. Planck released information on dust polarization at high latitude [29] (hereafter PIP-XXX), and in particular examined a field centered on the BICEP2 region (but somewhat larger than it) finding a level of polarized dust emission at 353 GHz sufficient to explain the 150 GHz excess observed by BICEP2, with relatively low signal-to-noise.

Maps and preparation
Expected spatial and frequency spectra of dust
Consistency of BICEP2 and Keck Array spectra
Alternative power spectrum estimation
Algorithm
Fiducial analysis
Variations from the fiducial data set and model
Validation with simulations
Subtraction of scaled spectra
POSSIBLE CAUSES OF DECORRELATION
Spatially varying dust frequency spectrum
CONCLUSIONS
Full Text
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