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Detectability of Gravitational Waves from the Coalescence of Massive Primordial Black Holes with Initial Clustering

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arxiv 1903.07337 v2 pith:BWYAPAO3 submitted 2019-03-18 astro-ph.CO gr-qchep-th

Detectability of Gravitational Waves from the Coalescence of Massive Primordial Black Holes with Initial Clustering

Qianhang Ding , Tomohiro Nakama , Joseph Silk , Yi Wang This is my paper
classification astro-ph.CO gr-qchep-th
keywords clusteringcoalescenceinitialblackgravitationalmassiveprimordialdecigo
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We show that the effect of initial non-Gaussian clustering can significantly enhance the event rate for primordial black hole (PBH) coalescence. The impact of such clustering is studied in a specific scenario of multi-stream inflation. Initial clustering enables the possibility of detecting massive PBH coalescence by space-based gravitational wave interferometers such as LISA and DECIGO/BBO. The parameter regime for the ground-based detectors to detect PBH coalescence is also extended.

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Cited by 3 Pith papers

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

  1. Smoluchowski Coagulation Equation and the Evolution of Primordial Black Hole Clusters

    astro-ph.CO 2026-04 unverdicted novelty 6.0

    Monte Carlo solutions to the Smoluchowski coagulation equation yield runaway timescales and mass evolution for primordial black hole clusters at different redshifts based on cluster properties.

  2. Merger rate of initially clustered primordial black holes for the two-body channel

    astro-ph.CO 2026-04 unverdicted novelty 5.0

    Weak initial clustering of primordial black holes reduces the PBH fraction needed to explain LIGO-Virgo-KAGRA mergers in the two-body channel, while strong clustering suppresses the rate via three-body formation.

  3. 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.