English

Primordial non-Gaussianity and non-Gaussian Covariance

Cosmology and Nongalactic Astrophysics 2023-02-08 v1

Abstract

In the pursuit of primordial non-Gaussianities, we hope to access smaller scales across larger comoving volumes. At low redshift, the search for primordial non-Gaussianities is hindered by gravitational collapse, to which we often associate a scale kNLk_{\rm NL}. Beyond these scales, it will be hard to reconstruct the modes sensitive to the primordial distribution. When forecasting future constraints on the amplitude of primordial non-Gaussianity, fNLf_{\rm NL}, off-diagonal components are usually neglected in the covariance because these are small compared to the diagonal. We show that the induced non-Gaussian off-diagonal components in the covariance degrade forecast constraints on primordial non-Gaussianity, even when all modes are well within what is usually considered the linear regime. As a testing ground, we examine the effects of these off-diagonal components on the constraining power of the matter bispectrum on fNLf_{\rm NL} as a function of kmaxk_{\rm max} and redshift, confirming our results against N-body simulations out to redshift z=10z=10. We then consider these effects on the hydrogen bispectrum as observed from a PUMA-like 21-cm intensity mapping survey at redshifts 2<z<62<z<6 and show that not including off-diagonal covariance over-predicts the constraining power on fNLf_{\rm NL} by up to a factor of 55. For future surveys targeting even higher redshifts, such as Cosmic Dawn and the Dark Ages, which are considered ultimate surveys for primordial non-Gaussianity, we predict that non-Gaussian covariance would severely limit prospects to constrain fNLf_{\rm NL} from the bispectrum.

Keywords

Cite

@article{arxiv.2206.10458,
  title  = {Primordial non-Gaussianity and non-Gaussian Covariance},
  author = {Thomas Flöss and Matteo Biagetti and P. Daniel Meerburg},
  journal= {arXiv preprint arXiv:2206.10458},
  year   = {2023}
}

Comments

6+6 pages, 4 figures, code available at https://github.com/tsfloss/pyNG