Pith. sign in

REVIEW 1 cited by

The Fates of Merging Supermassive Black Holes and a Proposal for a New Class of X-Ray Sources

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2004.06083 v3 pith:A7WTXKVW submitted 2020-04-13 astro-ph.GA

classification astro-ph.GA
keywords mathrmx-raycentralhostodotorbitingbinaryblack
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

We perform N-body simulations on some of the most massive galaxies extracted from a cosmological simulation of hierarchical structure formation with total masses in the range $10^{12} M_{\odot} < M_{tot} < 3\times 10^{13} M_{\odot}$ from $4\geq z \geq 0$. After galactic mergers, we track the dynamical evolution of the infalling black holes (BHs) around their host's central BHs. From 11 different simulations, we find that, of the 86 infalling BHs with masses > $10^4 M_{\odot}$, 36 merge with their host's central BH, 13 are ejected from their host galaxy, and 37 are still orbiting at $z=0$. Across all galaxies, 33 BHs are kicked to a higher orbit after close interactions with the central BH binary or multiple, after which only one of them merged with their hosts. These orbiting BHs should be detectable by their anomalous (not Low Mass X-ray Binary) spectra. The X-ray luminosities of the orbiting massive BHs at z=0 are in the range $10^{28}-10^{43}$ $\mathrm{erg}~\mathrm{s}^{-1}$, with a currently undetectable median value of $10^{33}$ $\mathrm{erg}~\mathrm{s}^{-1}$. However, the most luminous $\sim$5\% should be detectable by existing X-ray facilities.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. NPO: Learning Alignment and Meta-Alignment through Structured Human Feedback

    cs.AI 2025-07 reject novelty 3.0 of 10

    NPO formalizes alignment as a feedback-driven loss and claims meta-alignment reduces to it, but the formal results are definitional and the empirical claims are unsupported.

Pith tools