Nonlinear statistics of primordial black holes from Gaussian curvature perturbations

Cristiano Germani and Ravi K. Sheth
Phys. Rev. D 101, 063520 – Published 19 March 2020

Abstract

We develop the nonlinear statistics of primordial black holes generated by a Gaussian spectrum of primordial curvature perturbations. This is done by employing the compaction function as the main statistical variable under the constraints that (i) the overdensity has a high peak at a point x0, (ii) the compaction function has a maximum at a smoothing scale R, and finally, (iii) the compaction function amplitude at its maximum is higher than the threshold necessary to trigger a gravitational collapse into a black hole of the initial overdensity. Our calculation allows for the fact that the patches which are destined to form PBHs may have a variety of profile shapes and sizes. The predicted PBH abundances depend on the power spectrum of primordial fluctuations. For a very peaked power spectrum, our nonlinear statistics, the one based on the linear overdensity and the one based on the use of curvature perturbations, all predict a narrow distribution of PBH masses and comparable abundance. For broader power spectra, the linear overdensity statistics overestimate the abundance of primordial black holes while the curvature-based approach under-estimates it. Additionally, for very large smoothing scales, the abundance is no longer dominated by the contribution of a mean overdensity but rather by the whole statistical realizations of it.

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  • Received 11 February 2020
  • Accepted 2 March 2020

DOI:https://doi.org/10.1103/PhysRevD.101.063520

© 2020 American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Cristiano Germani1,* and Ravi K. Sheth2,†

  • 1Institut de Ciències del Cosmos, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain
  • 2Center for Particle Cosmology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

  • *germani@icc.ub.edu
  • shethrk@upenn.edu

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Vol. 101, Iss. 6 — 15 March 2020

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