The merger of a black hole with a cosmological horizon

Abstract In recent years there have been many studies on exactly solvable black hole mergers, based on
a model by Emparan and Martínez where the mass of one black hole is blown up to infinity.
Here we replace the large black hole by a cosmological horizon, and study how it merges with
a black hole in the Schwarzschild-de Sitter spacetime by considering an observer positioned at
future null infinity. We describe the geometry of the horizon over time, including the role that
caustics play in the merger process, and also examine the growth of the horizon area. We argue
that in the limit of zero cosmological constant, the system reduces to the Emparan-Martínez
Schwarzschild merger. This allows us to regularise the increase in the area during the merger,
which otherwise diverges.

Publication Details

Journal
Apollo (University of Cambridge)
Published
2025-04-25
DOI
https://doi.org/10.17863/cam.117666
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

The merger of a black hole with a cosmological horizon

Apollo (University of Cambridge)
Advanced Memory and Neural Computing
article

The merger of a black hole with a cosmological horizon

article en

Abstract

Abstract In recent years there have been many studies on exactly solvable black hole mergers, based on
a model by Emparan and Martínez where the mass of one black hole is blown up to infinity.
Here we replace the large black hole by a cosmological horizon, and study how it merges with
a black hole in the Schwarzschild-de Sitter spacetime by considering an observer positioned at
future null infinity. We describe the geometry of the horizon over time, including the role that
caustics play in the merger process, and also examine the growth of the horizon area. We argue
that in the limit of zero cosmological constant, the system reduces to the Emparan-Martínez
Schwarzschild merger. This allows us to regularise the increase in the area during the merger,
which otherwise diverges.

Apollo (University of Cambridge)
Openalex Percentile: Top 55%
Advanced Memory and Neural Computing
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