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arxiv: 2605.04852 · v1 · submitted 2026-05-06 · 🌌 astro-ph.HE

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Bayesian parameter estimation for the Core-bounce phase of Rapidly Rotating Core-Collapse Supernovae in real interferometric data

Emmanuel Avila , Michele Zanolin , Javier M. Antelis , Claudia Moreno
Authors on Pith no claims yet

Pith reviewed 2026-05-08 16:06 UTC · model grok-4.3

classification 🌌 astro-ph.HE
keywords gravitational wavescore-collapse supernovaeBayesian parameter estimationrotating progenitorscore-bouncephenomenological modelLIGO O3 data
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The pith

An extended phenomenological model recovers the rotational energy ratio in core-bounce supernova signals from real gravitational-wave noise.

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

The paper introduces a Bayesian approach to measure how much of a supernova progenitor's energy is in rotation by analyzing the gravitational-wave burst at core bounce. It builds on an earlier template by adding a single parameter to better capture how long the signal lasts. This change raises the median match to numerical simulations from 88.88 to 90.83 percent. Tests with hundreds of injected signals in actual LIGO O3a noise show that the rotation ratio can be recovered to a median accuracy of roughly 12 percent, better than earlier methods. The analysis also quantifies how detector noise creates biases and how choosing better priors can reduce them.

Core claim

We extend a previous phenomenological model of the core-bounce gravitational-wave signal by introducing an additional parameter that captures the signal timescale. The improved model achieves a higher median fitting factor of 90.83% when compared to numerical waveform databases. Parameter estimation via Markov Chain Monte Carlo on real O3a L1 noise recovers the rotational parameter β for 452 simulated signals with a median relative error of 11.93% and an uncertainty of 1.083 × 10^{-3} at 10 kpc. Real interferometric noise can introduce biases up to 11.9%, reducible to 0.6% with optimized priors.

What carries the argument

Extended phenomenological template that includes a timescale parameter to model the core-bounce gravitational-wave signal from rapidly rotating stars.

If this is right

  • Improved waveform templates enable more precise extraction of progenitor properties from gravitational-wave observations of supernovae.
  • Bayesian methods applied to real noise data provide realistic uncertainty estimates for the rotational energy ratio.
  • Accounting for noise biases is essential when applying such models to actual detector data.
  • Optimized priors can enhance the accuracy of parameter recovery without changing the model.

Where Pith is reading between the lines

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

  • Applying this method to actual detected events could help distinguish between different supernova explosion mechanisms.
  • Future gravitational-wave detectors might resolve smaller errors in β, allowing tests of specific equations of state.
  • Combining this with electromagnetic or neutrino observations could give a fuller picture of the progenitor star's rotation.

Load-bearing premise

The extended phenomenological model with the added timescale parameter adequately approximates the true core-bounce waveforms from numerical relativity simulations across the relevant range of progenitor parameters and equations of state.

What would settle it

A direct comparison of recovered β values against the known input values from a new set of numerical relativity simulations spanning different progenitor masses and equations of state would test if the reported errors and uncertainties hold.

Figures

Figures reproduced from arXiv: 2605.04852 by Claudia Moreno, Emmanuel Avila, Javier M. Antelis, Michele Zanolin.

Figure 1
Figure 1. Figure 1: Set of 126 waveforms from Richers Catalog (bottom) view at source ↗
Figure 2
Figure 2. Figure 2: Waveforms generated for the paper by Mitra, et al. view at source ↗
Figure 3
Figure 3. Figure 3: Histogram for the Fitting Factors between signals with view at source ↗
Figure 5
Figure 5. Figure 5: Curve of the noise model in L1 of Advanced LIGO view at source ↗
Figure 6
Figure 6. Figure 6: Histogram for the fitting factors of s fixed and s free to vary with their respect mean and median values, including the post-bounce oscillations (left) and excluding them (right). The bottom panel in the figure compares the fitting factors, considering the case if both were equal (red dashed line). . We see in the histograms of figure 6 that in the case where we consider s as a free parameter, there is a … view at source ↗
Figure 7
Figure 7. Figure 7: (Left)Base 10 Log of Bayes factors for each model with different number of parameters (color code) and only noise. view at source ↗
Figure 8
Figure 8. Figure 8: Prior probability density functions to test prior view at source ↗
Figure 9
Figure 9. Figure 9: Posterior PDFs at 10 kpc for 1000 samples in the MC-MC run. In LIGO Gaussian colored noise. Injected values are view at source ↗
Figure 10
Figure 10. Figure 10: Posterior PDFs at 10 kpc for 1000 samples in the MC-MC run. Injected values are the red dashed lines. Corresponding view at source ↗
Figure 11
Figure 11. Figure 11: Estimated value βˆ vs. β at bounce at 5 kpc (left) and 10 kpc (right). The black dashed line is βˆ = β view at source ↗
Figure 12
Figure 12. Figure 12: Fit of the Power Law α(β) = AβB + C, for the Noiseless case, 1, 5 and 10 kpc view at source ↗
Figure 13
Figure 13. Figure 13: Confusion matrices for the EOS classification. The Noiseless case, 1kpc, 5kpc and 10 kpc. view at source ↗
read the original abstract

We present a novel methodology to estimate the ratio of kinetic to gravitational potential energy in core-collapse supernova progenitors and to assess the equation of state (EOS) using gravitational-wave signals from the core-bounce phase of rapidly rotating stars in real interferometric data. We extend a previous phenomenological model by introducing an additional parameter that captures the signal timescale. The agreement between our template and numerical waveform databases is evaluated through fitting factors and Bayesian model comparison, also assessing consistency across datasets. The improved model increases the median fitting factor from 88.88% to 90.83%. Parameter estimation is performed via Markov Chain Monte Carlo using real O3aL1 noise. For 452 simulated signals, the rotational parameter $\beta$ is recovered with a median relative error of 11.93% (95th percentile: 38.41%) and an uncertainty of $\sigma_\beta = 1.083 \times 10^{-3}$ at 10 kpc, improving over previous matched-filtering results. We further analyze the impact of prior choices and noise properties, finding that real interferometric noise introduces biases up to 11.9%, while optimized priors can reduce them to 0.6%.

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

3 major / 2 minor

Summary. The manuscript extends a phenomenological model for the core-bounce gravitational-wave signal from rapidly rotating core-collapse supernovae by adding a signal timescale parameter. It reports improved median fitting factors (88.88% to 90.83%) against numerical-relativity databases and performs MCMC-based Bayesian parameter estimation on 452 simulated signals injected into real O3aL1 noise, recovering the rotational parameter β with median relative error 11.93% (95th percentile 38.41%) and uncertainty σ_β = 1.083 × 10^{-3} at 10 kpc, while analyzing prior and noise effects.

Significance. If the extended model proves sufficiently faithful to NR waveforms and the recovery statistics hold under independent tests, the work would offer a concrete Bayesian pipeline for extracting β and EOS information from future CCSN detections in real interferometric data, improving on matched-filtering baselines. The provision of quantitative metrics from injections into actual detector noise is a positive step toward practical applicability.

major comments (3)
  1. [parameter estimation results] Parameter estimation section (results on 452 injections): the manuscript must explicitly state the origin of the 452 simulated signals (whether generated from the extended phenomenological model itself or from independent NR simulations). If the former, the reported median relative error of 11.93% for β and the bias figures (up to 11.9% from real noise) only characterize statistical performance inside the model manifold and leave untested the propagation of residual mismatch (even at the improved 90.83% fitting factor) into systematic offsets in recovered β. This is load-bearing for the claim that the method is ready for real data.
  2. [fitting factors and Bayesian model comparison] Fitting-factor and model-comparison section: the coverage of the NR waveform database (progenitor masses, rotation rates, EOS variants) and any post-hoc selection criteria applied to the 452 cases or the fitting-factor calculations are not detailed. Without this, it is difficult to assess whether the median improvement from 88.88% to 90.83% and the consistency across datasets are representative or could be affected by limited sampling.
  3. [abstract and prior/noise analysis] Abstract and results on prior/noise impact: the claim that optimized priors reduce biases to 0.6% while real noise introduces up to 11.9% requires explicit linkage to the corresponding posterior distributions or tables; the current presentation leaves unclear how these percentages are computed (e.g., median absolute relative deviation from injected values) and whether they incorporate the new timescale parameter.
minor comments (2)
  1. [model extension] Notation for the new timescale parameter should be introduced with a clear symbol and units in the model section to avoid ambiguity when comparing to prior work.
  2. [abstract] The abstract states improvement over 'previous matched-filtering results' without a specific citation or quantitative comparison table; adding this would strengthen the claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough and constructive review of our manuscript. We address each of the major comments in detail below, providing clarifications and committing to revisions where necessary to enhance the manuscript's clarity and rigor.

read point-by-point responses

  1. Referee: Parameter estimation section (results on 452 injections): the manuscript must explicitly state the origin of the 452 simulated signals (whether generated from the extended phenomenological model itself or from independent NR simulations). If the former, the reported median relative error of 11.93% for β and the bias figures (up to 11.9% from real noise) only characterize statistical performance inside the model manifold and leave untested the propagation of residual mismatch (even at the improved 90.83% fitting factor) into systematic offsets in recovered β. This is load-bearing for the claim that the method is ready for real data.

    Authors: The 452 simulated signals were generated from the extended phenomenological model itself, with parameters sampled across the relevant physical parameter space covered by the NR database. We will explicitly state this origin in the revised Parameter Estimation section. While we agree that this means the recovery statistics characterize performance within the model manifold, the high fitting factors (median 90.83%) indicate that the template is a good approximation to NR waveforms, minimizing the impact of mismatch. The biases reported (up to 11.9% from real noise) are measured in the presence of actual detector noise. We will add a new paragraph discussing the potential for systematic errors due to model mismatch and how the fitting factor relates to expected parameter biases, thereby addressing the readiness for real data. revision: partial

  2. Referee: Fitting-factor and model-comparison section: the coverage of the NR waveform database (progenitor masses, rotation rates, EOS variants) and any post-hoc selection criteria applied to the 452 cases or the fitting-factor calculations are not detailed. Without this, it is difficult to assess whether the median improvement from 88.88% to 90.83% and the consistency across datasets are representative or could be affected by limited sampling.

    Authors: We will expand the Fitting-factor and model-comparison section to provide details on the coverage of the NR waveform database in terms of progenitor masses, rotation rates, and EOS variants. The 452 cases for the parameter estimation were selected to be representative of the database, with no post-hoc selection criteria applied beyond the requirements for injection into real noise data. We will include this information to allow assessment of representativeness. revision: yes

  3. Referee: Abstract and results on prior/noise impact: the claim that optimized priors reduce biases to 0.6% while real noise introduces up to 11.9% requires explicit linkage to the corresponding posterior distributions or tables; the current presentation leaves unclear how these percentages are computed (e.g., median absolute relative deviation from injected values) and whether they incorporate the new timescale parameter.

    Authors: We will revise both the abstract and the results section on prior/noise impact to include direct references to the relevant figures and tables showing the posterior distributions. The bias figures are computed as the median absolute relative error across all 452 injections. These calculations fully incorporate the new timescale parameter as it is part of the extended model used in all MCMC runs. We will add explicit text clarifying the definition of the bias metric and confirm the inclusion of the timescale parameter. revision: yes

Circularity Check

0 steps flagged

Standard MCMC recovery on model-generated injections; fitting factors evaluated separately against NR databases

full rationale

The paper extends a prior phenomenological model (with an added timescale parameter) and quantifies its agreement to numerical-relativity databases via fitting factors, raising the median from 88.88% to 90.83%. It then performs standard Bayesian MCMC parameter estimation on 452 simulated signals injected into real O3aL1 noise, reporting median relative error 11.93% and uncertainty for the rotational parameter β. These recovery statistics characterize statistical performance and noise biases inside the model manifold; they are obtained from explicit MCMC sampling rather than reducing by construction to any fitted parameter or self-citation. The central claims rest on independent numerical results (fitting-factor computations and MCMC chains) and do not invoke load-bearing self-citations or uniqueness theorems. Minor self-citation to the base model exists but is not load-bearing for the reported improvements or error figures.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the extended phenomenological template captures the essential features of numerical core-bounce waveforms and on standard gravitational-wave data-analysis assumptions such as stationary Gaussian noise statistics.

free parameters (1)
  • signal timescale parameter
    New parameter introduced to capture the duration of the core-bounce signal in the extended phenomenological model.
axioms (1)
  • domain assumption Core-bounce gravitational-wave signals from rapidly rotating progenitors can be adequately represented by the extended phenomenological template across the relevant parameter space
    Invoked for fitting-factor evaluation and for the validity of the subsequent Bayesian parameter estimation.

pith-pipeline@v0.9.0 · 5529 in / 1347 out tokens · 51501 ms · 2026-05-08T16:06:12.508077+00:00 · methodology

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Reference graph

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