Machine learning extracts core rotation and signal properties from CCSN gravitational waves, with next-generation detectors constraining rotation beyond 100 kpc for favorable orientations despite some uncertainties.
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Basilic is an end-to-end Bayesian pipeline for gravitational-wave burst inference and model classification, with a case study showing signal degeneracies between binary black hole mergers and cosmic strings.
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Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors
Machine learning extracts core rotation and signal properties from CCSN gravitational waves, with next-generation detectors constraining rotation beyond 100 kpc for favorable orientations despite some uncertainties.
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Basilic: An end-to-end pipeline for Bayesian burst inference and model classification in gravitational-wave data
Basilic is an end-to-end Bayesian pipeline for gravitational-wave burst inference and model classification, with a case study showing signal degeneracies between binary black hole mergers and cosmic strings.