Basilic: An end-to-end pipeline for Bayesian burst inference and model classification in gravitational-wave data

TL;DR

Basilic is a comprehensive pipeline built on bilby for Bayesian analysis of gravitational-wave burst signals, enabling efficient model selection and parameter estimation with practical features like modularity and cloud integration.

Contribution

It introduces Basilic, a modular, user-friendly pipeline for Bayesian burst analysis in gravitational-wave data, with example case studies and integration capabilities.

Findings

High anti-aligned spins can cause degeneracy between binary black hole and cosmic string signals.

Basilic demonstrates rapid, minimal-overhead analysis for gravitational-wave burst signals.

The pipeline's design facilitates future low-SNR detection studies.

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, pre-implemented 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 anti-aligned component spins, even at moderate mass, can result in a similar degeneracy. Motivated by the likely low-SNR expected regime of possible future detections, we propose a data-driven study of model degeneracy, to be employed in the event of an inconclusive Bayes factor.