Primordial non-Gaussianity and non-Gaussian Covariance

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 $k_{\mathrm{NL}}$. 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, $f_{\mathrm{NL}}$, 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 $f_{\mathrm{NL}}$ as a function of $k_{\max }$ and redshift, confirming our results against N-body simulations out to redshift $z=10$. We then consider these effects on the hydrogen bispectrum as observed from a PUMA-like 21-cm intensity mapping survey at redshifts $2<z<6$ and show that not including off-diagonal covariance over-predicts the constraining power on $f_{\mathrm{NL}}$ by up to a factor of $5$. 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 $f_{\mathrm{NL}}$ from the bispectrum.

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

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• Euclid: The search for primordial features
• Cosmological constraints from the power spectrum and bispectrum of 21cm intensity maps
• Improving constraints on primordial non-Gaussianity using neural network based reconstruction
• Constraining $f_{NL}$ using the Large-Scale Modulation of Small-Scale Statistics
• Neural Network Reconstruction of Non-Gaussian Initial Conditions from Dark Matter Halos
• Squeezing information from radio surveys to probe the primordial Universe
• Towards detecting Primordial non-Gaussianity in the CMB using Spherical Convolutional Neural Networks