CWRU PAT Coffee Agenda

Tuesdays 10:30 - 11:30 | Fridays 11:30 - 12:30

+1 Theoretical and numerical perspectives on cosmic distance averages.

cjc5 +1

-1 Monopole Fluctuation of the CMB and its Gauge Invariance.

cjc5 -1

Showing votes from 2020-12-11 12:30 to 2020-12-15 11:30 | Next meeting is Friday Dec 26th, 11:30 am.

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astro-ph.CO

  • Theoretical and numerical perspectives on cosmic distance averages.- [PDF] - [Article]

    Michel-Andrès Breton, Pierre Fleury
     

    The interpretation of cosmological observations relies on a notion of average Universe, which is usually taken as the homogeneous and isotropic Friedmann-Lema\^itre-Robertson-Walker (FLRW) model. However, inhomogeneities may statistically bias the observational averages with respect to FLRW, notably for distance measurements, due to a number of effects such as gravitational lensing and redshift perturbations. In this article, we review the main known theoretical results on average distance measures in cosmology, based on second-order perturbation theory, and fill some of their gaps. We then comprehensively test these theoretical predictions against ray tracing in a high-resolution dark-matter $N$-body simulation. This method allows us to describe the effect of small-scale inhomogeneities deep into the non-linear regime of structure formation, on light propagation up to $z=10$. We find that numerical results are in remarkably good agreement with theoretical predictions, in the limit of super-sample variance. No unexpectedly large bias originates from the very small scales, whose effect is fully encoded in the non-linear power spectrum. Specifically, the directional average of the inverse amplification and the source-averaged amplification are compatible with unity; the change in area of surfaces of constant cosmic time is compatible with zero; the biases on other distance measures, which can reach $\sim 10^{-3}$ at high redshift, are well understood. As a side product, we also confront the predictions of the recent finite-beam formalism with numerical data, and find excellent agreement.

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