End-to-end pipeline for Bayesian burst inference and model classification in gravitational-wave data
Phys. Rev. D 114, 044086 – Published 25 August, 2026
DOI: https://doi.org/10.1103/qfjy-59d2
Abstract
We present basilic, a dedicated pipeline for Bayesian model selection and parameter estimation of short-duration gravitational-wave burst signals observable with ground-based detectors. Built on top of the bilby framework, basilic combines modularity, preimplemented burst models, and htcondor integration to enable rapid, user-friendly analyses with minimal technical overhead. This work outlines the design philosophy, operational flow, and a set of example use cases demonstrating its scientific potential. As a case study, we also undertake an in-depth exploration of the comparison between a binary black hole merger and a cosmic-string signal, through a parameter space exploration injection campaign. In addition to the well-known high-mass binary black-hole signal morphology degeneracy with cosmic-string-like signals, we find that high antialigned component spins, even at moderate mass, can result in a similar degeneracy. Motivated by the likely low-SNR regime expected of possible future detections, we propose a data-driven study of model degeneracy, to be employed in the event of an inconclusive Bayes factor.