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Dark Matter Simulations with Primordial Black Holes in the Early Universe

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arxiv 2009.07813 v2 pith:J7QLZWII submitted 2020-09-16 astro-ph.CO gr-qchep-ph

Dark Matter Simulations with Primordial Black Holes in the Early Universe

classification astro-ph.CO gr-qchep-ph
keywords darkmatterboundpairsblackeffectseventsformation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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abstract

Primordial Black Holes (PBH) with masses of order $10-30 M_\odot$ have been proposed as a possible explanation of the gravitational waves emission events recently discovered by the LIGO observatory. If true, then PBHs would constitute a sizeable fraction of the dark matter component in the Universe. Using a series of cosmological N-body simulations which include both dark matter and a variable fraction of PBHs ranging from $f_{PBH} = 10^{-4}$ to $f_{PBH} = 1$, we analyse the processes of formation and disruption of gravitationally bound PBH pairs, as well as the merging of both bound and unbound pairs, and estimate the probabilities of such events. We show that they are in good agreement with the constrains to the PBH abundance obtained by the LIGO and other research groups. We find that pair stability, while being a main factor responsible for the merger rate, is significantly affected by the effects of dark matter halo formation and clustering. As a side result, we also evaluate the effects of numerical errors in the stability of bound pairs, which can be useful for future research using this methodology.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Do Primordial Black Hole Clusters Survive the Galaxy? Collisional Disruption and Microlensing Implications

    astro-ph.CO 2026-07 conditional novelty 6.0

    Cluster-cluster collisions strip 50–96% of 10^6–10^7 M⊙ PBH cluster mass by z=0, so microlensing sightlines to the Magellanic Clouds are ~49–92% smooth.

  2. Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective

    hep-ph 2026-04 unverdicted novelty 3.0

    A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.