Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-10T07:12:25.574Z Has data issue: false hasContentIssue false

The Effect of Non-Gaussian Primordial Perturbations on Large-Scale Structure

Published online by Cambridge University Press:  20 January 2023

G. A. Peña
Affiliation:
Instituto de Física y Astronomía, Universidad de Valparaíso, Gran Bretaña 1111, Valparaíso, Chile email: greco.pena@postgrado.uv.cl
G. N. Candlish
Affiliation:
Instituto de Física y Astronomía, Universidad de Valparaíso, Gran Bretaña 1111, Valparaíso, Chile email: greco.pena@postgrado.uv.cl
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The late-time effect of primordial non-Gaussianity offers a window into the physics of inflation and the very early Universe. In this work we study the consequences of a particular class of primordial non-Gaussianity that is fully characterized by initial density fluctuations drawn from a non-Gaussian probability density function, rather than by construction of a particular form for the primordial bispectrum. We numerically generate multiple realisations of cosmological structure and use the late-time matter polyspectra to determine the effect of these modified initial conditions. In this non-Gaussianity has only a small imprint on the first polyspectra, when compared to a standard Gaussian cosmology. Furthermore, some of our models present an interesting scale-dependent deviation from the Gaussian case in the bispectrum and trispectrum, although the signal is at most at the percent level.

Type
Contributed Paper
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Astronomical Union

References

Celoria, M., & Matarrese, S. 2018, J. Cosmology Astropart. Phus., 039 Google Scholar
Chen, X., Palma, G. A., Scheihing, H. B., & Sypsas, S. 2018a, Phys. Rev. D., 98, 083528 CrossRefGoogle Scholar
Chen, X., Palma, G. A., Scheihing, H. B., & Sypsas, S. 2018, Phys. Rev. Lett., 121, 161302 CrossRefGoogle Scholar
Hahn, O. & Abel, T. 2011, MNRAS, 415, 3 CrossRefGoogle Scholar
Howlett, C., Manera, M., & Percival, W. J. 2015, Astronomy and Computing, 12, 109 CrossRefGoogle Scholar
Sefusatti, E., Crocce, M., Scoccimarro, R., & Couchman, H. M. P. 2016, MNRAS, 460, 3624 CrossRefGoogle Scholar
Teyssier, R. 2002, A&A, 382, 412 Google Scholar
Verde, L., & Heavens, A. F. 2001, ApJ, 553, 14 CrossRefGoogle Scholar
Villaescusa-Navarro, F. 2018, Pylians: Python libraries for the analysis of numerical simulations (ascl:1811.008)Google Scholar