Impact of Bayesian Priors on the Inferred Masses of Quasi-Circular Intermediate-Mass Black Hole Binaries
Koustav Chandra, Archana Pai, Samson H. W. Leong, Juan Calder\'on, Bustillo

TL;DR
This study examines how different Bayesian prior assumptions influence the inferred masses of intermediate-mass black hole binaries from gravitational wave data, highlighting the importance of prior choice in parameter estimation.
Contribution
It provides a controlled simulation-based analysis of prior effects on mass and distance estimates, emphasizing the need for careful prior selection in gravitational wave inference.
Findings
Posteriors depend on the assumed mass prior distribution.
Biases occur when pre-merger information is limited.
Flat priors on total mass and mass ratio are recommended.
Abstract
Observation of gravitational waves from inspiralling binary black holes has offered a unique opportunity to study the physical parameters of the component black holes. To infer these parameters, Bayesian methods are employed in conjunction with general relativistic waveform models that describe the source's inspiral, merger, and ringdown. The results depend not only on the accuracy of the waveform models but also on the underlying fiducial prior distribution used for the analysis. In particular, when the pre-merger phase of the signal is barely observable within the detectors' bandwidth, as is currently the case with intermediate-mass black hole binary signals in ground-based gravitational wave detectors, different prior assumptions can lead to different interpretations. In this study, we utilise the gravitational-wave inference library, , to evaluate the impact…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Model Reduction and Neural Networks
