Effects of Critical Collapse on Primordial Black-Hole Mass Spectra

TL;DR

This paper investigates how critical collapse scaling influences primordial black hole mass spectra, revealing that it causes a shift, broadening, and reduction in mass, which is crucial for accurate observational comparisons.

Contribution

It demonstrates the importance of including critical collapse effects in modeling primordial black hole mass spectra across various early Universe scenarios.

Findings

Critical collapse causes spectrum broadening and mass reduction.

Effects depend on specific models and parameters.

Ignoring these effects can lead to inaccurate observational constraints.

Abstract

Certain inflationary models as well as realisations of phase transitions in the early Universe predict the formation of primordial black holes. For most mass ranges, the fraction of matter in the form of primordial black holes is limited by many different observations on various scales. Primordial black holes are assumed to be formed when overdensities that cross the horizon have Schwarzschild radii larger than the horizon. Traditionally it was therefore assumed that primordial black-hole masses were equal to the horizon mass at their time of formation. However, detailed calculations of their collapse show that primordial black holes formed at each point in time should rather form a spectrum of different masses, obeying critical scaling. Though this has been known for more than fifteen years, the effect of this scaling behaviour is largely ignored when considering predictions for primordial black hole mass spectra. In this paper we consider the critical collapse scaling for a variety of models which produce primordial black holes, and find that it generally leads to a shift, broadening and an overall decrease of the mass contained in primordial black holes. This effect is model and parameter dependent and cannot be contained by a constant rescaling of the spectrum; it can become important and should be taken into account when comparing to observational constraints.