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arxiv: 2604.13839 · v1 · submitted 2026-04-15 · 🌀 gr-qc · astro-ph.HE

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

Iuliu Cuceu , Marie Anne Bizouard
Authors on Pith no claims yet

Pith reviewed 2026-05-10 12:49 UTC · model grok-4.3

classification 🌀 gr-qc astro-ph.HE
keywords gravitational wave burstsBayesian model selectionparameter estimationcosmic stringsbinary black holessignal degeneracylow signal-to-noise ratioinjection studies
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The pith

Basilic supplies an integrated pipeline for Bayesian classification and parameter estimation of short gravitational-wave burst signals.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Basilic as a ready-to-use tool that lets analysts run Bayesian model selection and parameter fits on brief bursts seen by ground-based detectors. It supplies pre-built signal models, modular structure, and support for distributed computing so that users can complete full analyses with little setup time. An injection study then maps how high-mass binary black hole signals overlap with cosmic-string waveforms and shows that high anti-aligned spins produce similar overlap even at moderate masses. The work also outlines a data-driven check to be used when the Bayes factor between models stays inconclusive, which is the regime expected for most future detections. A sympathetic reader would care because misclassifying the origin of a weak burst could waste follow-up resources or miss evidence for new physics.

Core claim

Basilic combines modularity, pre-implemented burst models, and distributed-computing integration to enable rapid Bayesian model selection and parameter estimation for short-duration gravitational-wave bursts. An extensive injection campaign demonstrates the known degeneracy between high-mass binary black hole mergers and cosmic-string signals and additionally reveals that high anti-aligned component spins can produce comparable signal morphology even at moderate masses. Motivated by the low signal-to-noise ratios anticipated for future detections, the authors propose a data-driven procedure for quantifying model degeneracy whenever the Bayes factor between competing models remains indecisive

What carries the argument

The Basilic pipeline, which supplies modular pre-implemented burst models together with Bayesian inference and distributed job management to run end-to-end model selection and parameter estimation on burst data.

If this is right

  • Analyses of burst candidates can be completed rapidly with minimal custom coding.
  • The parameter-space overlap between high-mass binary black hole and cosmic-string signals is quantified across a broad range of masses and spins.
  • High anti-aligned spins are shown to create an additional route to signal degeneracy at moderate masses.
  • A data-driven procedure is supplied for assessing model choice when the Bayes factor is inconclusive.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar modular pipelines could be adapted to classify other transient signals whose origins are uncertain in noisy data.
  • The degeneracy maps could guide which additional observables, such as spin estimates, future detectors should prioritize to break model ambiguities.
  • Routine use of the data-driven degeneracy check might reduce the number of ambiguous events that require expensive follow-up observations.

Load-bearing premise

The pre-implemented burst models are complete enough to represent real signals and the degeneracies found in simulations continue to hold under realistic detector noise at low signal strength.

What would settle it

Apply the pipeline to a set of injected signals whose waveforms lie outside all pre-implemented models; if the pipeline still reports decisive Bayes factors favoring one of the available models, the claim of reliable classification is falsified.

Figures

Figures reproduced from arXiv: 2604.13839 by Iuliu Cuceu, Marie Anne Bizouard.

Figure 1
Figure 1. Figure 1: FIG. 1. SNR density, constructed from aggregating all the [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Bayes factor vs Bayes factor plots of various analysis combinations, comparing the evidence for the BBH hypothesis [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Appendix B: Morphological analysis As injection campaigns are such an integral part of Basilic, we will address here explicitly how injections can be used to address model confusion and provide con￾text for the interpretation of Bayes factors and poten￾tial aid in model selection. In particular, we are in￾terested in scenarios where the between-models Bayes factors are unconclusive (log10 BF ∼ 1 − 3), whil… view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Part of the [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces Basilic, a modular end-to-end pipeline built on the bilby framework for Bayesian parameter estimation and model selection of short-duration gravitational-wave burst signals from ground-based detectors. It emphasizes usability through pre-implemented burst models and HTCondor integration, outlines the design and operational flow, and illustrates capabilities via example use cases, including a detailed parameter-space injection study comparing binary black hole (BBH) mergers to cosmic string signals that identifies an additional degeneracy for high anti-aligned spins at moderate masses and proposes a data-driven diagnostic for inconclusive Bayes factors in low-SNR regimes.

Significance. If the pipeline proves robust, Basilic could meaningfully lower the barrier to Bayesian burst analyses in the gravitational-wave community, particularly for rapid follow-up of candidate events where model classification is ambiguous. The reported spin-induced degeneracy extends existing knowledge of BBH-cosmic-string morphology overlaps and the data-driven diagnostic offers a practical response to low-SNR challenges. The modular architecture and HTCondor support are clear strengths for reproducibility and scalability.

major comments (1)
  1. [BBH-vs-cosmic-string case study] BBH-vs-cosmic-string case study (as described in the abstract and full text): The injection campaign draws all signals from the same pre-implemented burst models later used for recovery. This self-consistent setup does not test against independent waveform families, non-Gaussian noise realizations, or higher-order effects such as precession-induced amplitude modulations, which directly undermines the reliability of the claimed high anti-aligned spin degeneracy at moderate masses and the proposed data-driven degeneracy diagnostic for inconclusive Bayes factors.
minor comments (2)
  1. The description of the pipeline's operational flow would benefit from an explicit flowchart or schematic diagram to improve user accessibility.
  2. Documentation of the pre-implemented burst models should include explicit references to their source implementations or original papers for full transparency.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for recognizing the potential utility of the Basilic pipeline. We address the single major comment below.

read point-by-point responses

  1. Referee: The injection campaign draws all signals from the same pre-implemented burst models later used for recovery. This self-consistent setup does not test against independent waveform families, non-Gaussian noise realizations, or higher-order effects such as precession-induced amplitude modulations, which directly undermines the reliability of the claimed high anti-aligned spin degeneracy at moderate masses and the proposed data-driven degeneracy diagnostic for inconclusive Bayes factors.

    Authors: We agree that the injection-recovery campaign is performed within the same set of pre-implemented burst models. This choice is deliberate: the scientific goal of the case study is to map regions of parameter space where the BBH and cosmic-string models produce morphologically similar signals under identical modeling assumptions, thereby identifying the additional high anti-aligned spin degeneracy at moderate masses. Because both models belong to the same framework, the Bayes-factor comparison directly quantifies their overlap. We acknowledge, however, that this controlled setup does not address independent waveform families, non-Gaussian noise, or precession-induced modulations, and therefore cannot claim robustness for real-data applications. We will revise the manuscript to (i) explicitly state the illustrative scope of the study, (ii) temper the language around the reported degeneracy and the data-driven diagnostic, and (iii) note that the diagnostic is proposed as a practical, simulation-motivated tool whose performance in realistic conditions remains to be validated. These changes will be incorporated in the revised version. revision: partial

Circularity Check

0 steps flagged

No circularity: pipeline description and standard injection-recovery demonstration

full rationale

The paper is a software presentation of the Basilic pipeline built on the external bilby framework. It describes design, modularity, and a case study using injection-recovery with pre-implemented burst models. This is standard validation practice and does not involve any derivation chain, fitted parameters renamed as predictions, or self-citation that bears the central claim. No equations or load-bearing steps reduce to inputs by construction. The work is self-contained as a tool description relying on external frameworks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a software pipeline description, the central claim rests on standard Bayesian inference methods and gravitational-wave data analysis assumptions from prior literature rather than new physical axioms or fitted parameters.

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discussion (0)

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