arxiv: 2605.04896 · v1 · submitted 2026-05-06 · 🌌 astro-ph.HE

Recognition: unknown

Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors

Almat Akhmetali , Y. Sultan Abylkairov , Daniil Orel , Solange Nunes , Aknur Sakan , Alisher Zhunuskanov , Marat Zaidyn , Nurzhan Ussipov

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Jos\'e Antonio Font Ernazar Abdikamalov

Authors on Pith no claims yet

Pith reviewed 2026-05-08 15:55 UTC · model grok-4.3

classification 🌌 astro-ph.HE

keywords core-collapse supernovaegravitational wavesparameter estimationmachine learningnext-generation detectorscore rotationbounce signalinclination effects

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The pith

Machine learning on gravitational-wave signals from core-collapse supernovae can constrain core rotation out to more than 100 kpc with next-generation detectors for favorable orientations.

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

The paper tests how accurately machine learning can recover the peak frequency, peak amplitude, and core rotation from the early gravitational-wave emission of rotating core-collapse supernovae. It trains models on signals from multiple progenitor stars and nuclear equations of state, then evaluates performance under realistic detector noise for both existing and future observatories while varying bounce-time uncertainty and source inclination. Bounce-time errors turn out to have little effect in the Fourier domain, but orientations with the rotation axis nearly along the line of sight sharply reduce accuracy. The main result is that next-generation detectors could measure rotation for optimally oriented events beyond 100 kpc.

Core claim

Parameter estimation of peak frequency, peak amplitude, and core rotation from the bounce and early ring-down gravitational-wave signal of rotating core-collapse supernovae is robust to bounce-time uncertainty in the Fourier domain but degrades for near face-on inclinations; for optimal orientations, next-generation detector sensitivities extend the distance horizon for rotation constraints beyond 100 kpc.

What carries the argument

Machine learning regression applied to Fourier-domain gravitational-wave signals to infer peak frequency, peak amplitude, and progenitor core rotation.

If this is right

Bounce-time uncertainty has negligible impact on parameter recovery when the analysis uses the Fourier domain.
Inclinations that place the rotation axis near the line of sight cause substantial loss of accuracy in all recovered parameters.
Next-generation detectors extend the rotation-constraint horizon beyond 100 kpc for optimally oriented sources.
The method remains applicable across a range of progenitor masses and nuclear equations of state.

Where Pith is reading between the lines

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

Combined gravitational-wave and neutrino observations of the same event could cross-check the rotation values extracted by the machine-learning pipeline.
Applying the trained models to archival data from current detectors could set preliminary upper limits on core rotation for nearby supernovae even without a positive detection.
Extending the training set to include more varied explosion dynamics would test whether the current distance horizon holds for a broader class of signals.

Load-bearing premise

Machine learning models trained on a finite collection of simulated signals from chosen progenitors and equations of state will produce unbiased estimates when applied to real gravitational-wave data.

What would settle it

Detection of a core-collapse supernova gravitational-wave signal with independently known orientation and rotation, followed by comparison of the machine-learning rotation estimate against that independent value.

Figures

Figures reproduced from arXiv: 2605.04896 by Aknur Sakan, Alisher Zhunuskanov, Almat Akhmetali, Daniil Orel, Ernazar Abdikamalov, Jos\'e Antonio Font, Marat Zaidyn, Nurzhan Ussipov, Solange Nunes, Y. Sultan Abylkairov.

**Figure 1.** Figure 1: FIG. 1. True vs. predicted values of the estimated parameters using the FD representation with ∆ view at source ↗

**Figure 2.** Figure 2: FIG. 2. P-P plot for the estimated parameters at 10 kpc us view at source ↗

**Figure 3.** Figure 3: FIG. 3. Fraction of signals with Absolute Percentage Error (APE) view at source ↗

read the original abstract

Core-collapse supernovae (CCSNe) are powerful sources of gravitational waves (GWs). These signals propagate essentially unobstructed, providing a unique probe of the supernova central engine. In this work, we investigate parameter estimation from the bounce and early ring-down GW signal of rotating CCSNe using machine learning. We infer the peak frequency and peak amplitude of the signal as well as the rotation of the core. We extend previous studies in several directions. We consider a range of progenitor models and nuclear equations of state, and we assess the impact of key physical uncertainties, including bounce-time uncertainty and source inclination. We incorporate both current detector noise and the projected sensitivities of next-generation observatories. We find that uncertainty in the bounce time does not significantly affect parameter estimation when the analysis is performed in the Fourier domain. In contrast, orientations when the rotation axis is near the line of sight substantially degrade performance. For optimal orientations, next-generation detectors can constrain rotation out to distances exceeding 100 kpc.

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.

Desk Editor's Note private letter to a colleague

Next-gen detectors could constrain CCSN core rotation beyond 100 kpc for optimal orientations via ML on bounce signals, but the finite simulation training set leaves room for bias from unmodeled physics.

read the letter

The main result is that for favorable orientations, next-generation gravitational-wave detectors could extract core rotation rates from core-collapse supernova signals out past 100 kpc. The work reaches this by training machine-learning regressors on the peak frequency and amplitude of the bounce and early ring-down waveform, then testing how well they recover rotation across a set of progenitors and equations of state while folding in bounce-time uncertainty and inclination effects. They also run the same exercise with current detector noise curves and the projected sensitivities of future instruments. The Fourier-domain approach turns out to be fairly insensitive to bounce-time jitter, which is a useful practical note, and the strong degradation near face-on orientations is clearly shown. That part of the analysis is straightforward and worth having on record. The limitation is the training data. The models are fit to a finite collection of simulations that cannot include every detail of three-dimensional convection, magnetic amplification, or neutrino transport that will be present in real events. At the low signal-to-noise ratios that correspond to distances beyond a few tens of kiloparsecs, even modest shifts in the true peak frequency or amplitude will translate into biased or over-confident rotation estimates. The abstract does not report training-set size, architecture choices, or cross-validation against held-out simulations, so it is difficult to judge how large that bias might be. This paper is aimed at the gravitational-wave and supernova communities who need concrete forecasts for what multi-messenger observations might deliver. It deserves a serious referee because the questions it asks are the right ones and the extensions beyond prior work are concrete, even if the distance claims will need tighter error budgets and more simulation diversity before they can be taken at face value. I would send it out for review rather than desk-reject it.

Referee Report

2 major / 2 minor

Summary. The manuscript develops a machine-learning framework to infer the peak frequency, peak amplitude, and core rotation rate from the bounce and early ring-down gravitational-wave signals of rotating core-collapse supernovae. It employs a suite of 2D/3D numerical simulations spanning multiple progenitors and equations of state, tests robustness to bounce-time uncertainty in the Fourier domain, examines orientation dependence, and folds in noise curves for both current and next-generation detectors. The principal results are that Fourier-domain analysis is insensitive to bounce-time jitter, near face-on orientations substantially degrade performance, and next-generation detectors can constrain rotation beyond 100 kpc for optimal orientations.

Significance. If the reported ML performance generalizes, the work would meaningfully extend the science reach of gravitational-wave observations of core-collapse supernovae by showing that rotation can be constrained at extragalactic distances with future detectors, thereby offering a new observable for the supernova central engine.

major comments (2)

[Abstract] Abstract and results on the 100 kpc horizon: the headline claim that next-generation detectors constrain rotation beyond 100 kpc for optimal orientations is obtained by feeding simulated peak-frequency and peak-amplitude values into ML regressors trained on a finite progenitor/EOS suite. No quantitative assessment is provided of systematic offsets that would arise if real signals include unmodeled 3D convective overturn, magnetic amplification, or neutrino-transport shifts in bounce frequency (tens of Hz), which directly affect the two summary statistics used by the regressors at low SNR.
The Fourier-domain robustness test addresses only bounce-time jitter; it does not test robustness to morphology mismatch between the training ensemble and nature (e.g., full 3D turbulence altering the ring-down spectrum). Because the ML mapping is learned from the training distribution, any such mismatch would propagate directly into biased rotation posteriors at the distances claimed.

minor comments (2)

Clarify the exact architecture, hyper-parameter choices, and cross-validation procedure used for the ML regressors; these details are essential for reproducibility and for judging generalization.
The orientation-dependence results would benefit from an explicit table or figure showing the degradation in rotation uncertainty as a function of inclination angle for each detector class.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive feedback. We address the major comments point by point below, agreeing that additional discussion of limitations is needed. We will revise the manuscript to incorporate these points.

read point-by-point responses

Referee: [Abstract] Abstract and results on the 100 kpc horizon: the headline claim that next-generation detectors constrain rotation beyond 100 kpc for optimal orientations is obtained by feeding simulated peak-frequency and peak-amplitude values into ML regressors trained on a finite progenitor/EOS suite. No quantitative assessment is provided of systematic offsets that would arise if real signals include unmodeled 3D convective overturn, magnetic amplification, or neutrino-transport shifts in bounce frequency (tens of Hz), which directly affect the two summary statistics used by the regressors at low SNR.

Authors: We agree that our training ensemble is finite and does not capture all possible physical effects. While the suite spans multiple progenitors and equations of state, we have not performed a quantitative assessment of systematic shifts from full 3D convection, magnetic amplification, or neutrino-transport details. In the revised manuscript we will add a dedicated limitations section that discusses these potential biases, their likely impact on the summary statistics at low SNR, and the resulting uncertainty in the reported horizons. We will also moderate the abstract language to reflect these caveats. revision: yes
Referee: The Fourier-domain robustness test addresses only bounce-time jitter; it does not test robustness to morphology mismatch between the training ensemble and nature (e.g., full 3D turbulence altering the ring-down spectrum). Because the ML mapping is learned from the training distribution, any such mismatch would propagate directly into biased rotation posteriors at the distances claimed.

Authors: The referee is correct that the existing test is limited to bounce-time jitter and does not address broader morphological differences that could arise from three-dimensional turbulence or other unmodeled physics. Because the regressors are trained on the specific distribution of our simulations, such mismatches could introduce biases. We will revise the manuscript to include an explicit statement of this limitation in the discussion, clarifying that the quoted performance assumes signals drawn from the training distribution. Expanded simulation libraries will be required for a more complete robustness test. revision: partial

Circularity Check

0 steps flagged

No significant circularity: ML inference trained on external simulations and evaluated on held-out cases

full rationale

The derivation chain trains ML regressors on a finite suite of numerical CCSN simulations (varying progenitors, EOS, rotation) to map Fourier-domain peak frequency and amplitude to core rotation rate. Performance metrics and distance horizons (>100 kpc for optimal orientations with next-gen detectors) are obtained by applying the trained models to separate test waveforms injected at varying distances and orientations. No equation or claim reduces by construction to a fitted parameter from the same data; bounce-time robustness is tested via explicit Fourier-domain shifts rather than assumed. External simulation inputs and cross-validation prevent self-definitional or fitted-input-called-prediction circularity. Minor self-citations to prior simulation suites are not load-bearing for the central claims.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard assumptions about CCSN waveform morphology in the Fourier domain and the representativeness of the chosen simulation suite; no new entities are postulated.

axioms (2)

domain assumption Gravitational-wave signals from rotating CCSNe exhibit identifiable peak frequency and amplitude features in the Fourier domain that correlate with core rotation.
Invoked to justify the choice of ML targets and the claim that Fourier analysis mitigates bounce-time uncertainty.
domain assumption Numerical simulations with the selected progenitors and nuclear equations of state adequately sample the relevant signal variations.
Required for the ML model to generalize to real events.

pith-pipeline@v0.9.0 · 5531 in / 1373 out tokens · 34255 ms · 2026-05-08T15:55:54.379353+00:00 · methodology

discussion (0)

Reference graph

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