GW231123: Overlapping Gravitational Wave Signals?
Qian Hu, Harsh Narola, Jef Heynen, Mick Wright, John Veitch, Justin Janquart, and Chris Van Den Broeck

TL;DR
This paper investigates the gravitational wave event GW231123, proposing a model with overlapping signals that better explains the data and reduces measurement discrepancies, while considering astrophysical implications like lensing.
Contribution
It introduces a flexible overlapping signals model for GW data analysis, demonstrating its effectiveness in explaining GW231123 and addressing prior measurement inconsistencies.
Findings
Overlapping signal model is favored with Bayes factors of 10^2 to 10^4.
Discrepancies between waveform models are mitigated under the overlapping signals hypothesis.
Similar properties of the two sources suggest gravitational lensing as an alternative explanation.
Abstract
The recently discovered gravitational wave event GW231123 was interpreted as the merger of two black holes with a total mass of 190-265 , making it the heaviest such merger detected to date. Whilst much of the post-discovery literature has focused on its astrophysical origins, primary analyses have exhibited considerable discrepancies in the measurement of source properties between waveform models, which cannot reliably be reproduced by simulations. Such discrepancies may arise when an unaccounted overlapping signal is present in the data, or from phenomena that produce similar effects, such as gravitational lensing or overlapping noise artifacts. In this work, we analyse GW231123 using a flexible model that allows for two overlapping signals, and find that it is favoured over the isolated signal model with Bayes factors of , depending on the waveform model.…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Gamma-ray bursts and supernovae · Statistical Mechanics and Entropy
