Constraining the primordial black hole scenario with Bayesian inference and machine learning: the GWTC-2 gravitational wave catalog

TL;DR

This study uses Bayesian inference and machine learning on GWTC-2 data to assess if primordial black holes could account for dark matter, constraining their formation models and properties.

Contribution

It introduces a hierarchical Bayesian framework combined with deep learning to analyze gravitational wave data for primordial black hole signatures.

Findings

PBHs may constitute less than 0.3% of dark matter

Data favors PBH accretion and spin-up scenarios

PBH abundance remains compatible with existing constraints

Abstract

Primordial black holes (PBHs) might be formed in the early Universe and could comprise at least a fraction of the dark matter. Using the recently released GWTC-2 dataset from the third observing run of the LIGO-Virgo Collaboration, we investigate whether current observations are compatible with the hypothesis that all black hole mergers detected so far are of primordial origin. We constrain PBH formation models within a hierarchical Bayesian inference framework based on deep learning techniques, finding best-fit values for distinctive features of these models, including the PBH initial mass function, the fraction of PBHs in dark matter, and the accretion efficiency. The presence of several spinning binaries in the GWTC-2 dataset favors a scenario in which PBHs accrete and spin up. Our results indicate that PBHs may comprise only a fraction smaller than $0.3 \%$ of the total dark matter, and that the predicted PBH abundance is still compatible with other constraints.