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PreprintCC BY 4.0arXiv

Eccentricity in Disguise? Insights from GW231123 and Numerically Simulated Binary Black Hole Merger Signals

Abstract

GW231123 is a gravitational-wave signal originating from the merger of a black hole binary with total mass ∼ 250 M⊙, the largest ever detected by the LIGO-Virgo-Kagra Collaboration. Remarkably, under standard priors, the system features among the fastest-spinning binary components confidently measured in binary mergers, $ χ_{1,2} \gtrsim 0.7$ at 90% one-dimensional credibility, according to the most accurate model employed. As typical binary mergers result in remnants with χ∼ 0.7, such spin values are challenging to obtain even from previous (hierarchical) mergers. These inferred properties rely on waveform models lacking eccentric corrections in the merger-ringdown stage. Here, we show that binaries retaining significant eccentricity up to merger can be misinterpreted as near-extremally spinning when non-circular corrections are neglected. Binary-agnostic ringdown analysis instead provides unbiased estimates of the remnant properties, provided that a robust estimate of the signal peak can be obtained. We re-analyse GW231123 using available eccentric numerical-relativity catalogues, finding that although eccentric templates can provide a good fit to the data, quasi-spherical templates are still favoured. Ringdown analyses confirm a secondary likelihood peak correlated with large eccentricity values, but improved eccentric models will be required to assess the reliability of this interpretation. Finally, analysing GW231123 under population-informed parametric priors confirms the exceptional nature of this event within the current black hole binary population.

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BibTeX
@misc{chandra2026eccentricity,
  title = {{Eccentricity in Disguise? Insights from GW231123 and Numerically Simulated Binary Black Hole Merger Signals}},
  author = {Koustav Chandra and Johann Fernandes and Akshita Mittal and Gregorio Carullo},
  howpublished = {arXiv},
  year = {2026},
  month = jun,
  doi = {10.48550/arxiv.2606.15150},
  eprint = {2606.15150},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.15150},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Eccentricity in Disguise? Insights from GW231123 and Numerically Simulated Binary Black Hole Merger Signals
AU  - Koustav Chandra
AU  - Johann Fernandes
AU  - Akshita Mittal
AU  - Gregorio Carullo
PY  - 2026
DA  - 2026/06/13
PB  - arXiv
DO  - 10.48550/arxiv.2606.15150
UR  - https://arxiv.org/abs/2606.15150
AB  - GW231123 is a gravitational-wave signal originating from the merger of a black hole binary with total mass ∼ 250 M_⊙, the largest ever detected by the LIGO-Virgo-Kagra Collaboration. Remarkably, under standard priors, the system features among the fastest-spinning binary components confidently measured in binary mergers, $ χ_{1,2} \gtrsim 0.7$ at 90% one-dimensional credibility, according to the most accurate model employed. As typical binary mergers result in remnants with χ∼ 0.7, such spin values are challenging to obtain even from previous (hierarchical) mergers. These inferred properties rely on waveform models lacking eccentric corrections in the merger-ringdown stage. Here, we show that binaries retaining significant eccentricity up to merger can be misinterpreted as near-extremally spinning when non-circular corrections are neglected. Binary-agnostic ringdown analysis instead provides unbiased estimates of the remnant properties, provided that a robust estimate of the signal peak can be obtained. We re-analyse GW231123 using available eccentric numerical-relativity catalogues, finding that although eccentric templates can provide a good fit to the data, quasi-spherical templates are still favoured. Ringdown analyses confirm a secondary likelihood peak correlated with large eccentricity values, but improved eccentric models will be required to assess the reliability of this interpretation. Finally, analysing GW231123 under population-informed parametric priors confirms the exceptional nature of this event within the current black hole binary population.
ER  -