Pith. sign in

REVIEW 2 major objections 4 minor 3 cited by

Real noise, varied progenitors, and bounce-time uncertainty do not break machine-learning classification of the dense-matter equation of state from supernova gravitational waves.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.5

2026-07-13 16:48 UTC pith:T6R67TSL

load-bearing objection Solid incremental robustness check: real O4a noise, four progenitors, and 20 ms bounce jitter do not kill discrete EOS classification; dataset size helps more than progenitor diversity hurts. the 2 major comments →

arxiv 2603.27680 v1 pith:T6R67TSL submitted 2026-03-29 astro-ph.HE

Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves

Almat Akhmetali , Y. Sultan Abylkairov , Marat Zaidyn , Aknur Sakan , Alisher Zhunuskanov , Nurzhan Ussipov , Jos\'e Antonio Font , Alejandro Torres-Forn\'e
show 1 more author
Ernazar Abdikamalov
This is my paper
classification astro-ph.HE
keywords core-collapse supernovaegravitational wavesnuclear equation of statemachine learningsupport vector machineLIGO noiseprogenitor diversitybounce-time uncertainty
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Gravitational waves emitted at the bounce of a rotating stellar core carry information about the nuclear equation of state (EOS). Earlier machine-learning studies showed that EOS models can be classified from those signals, but only under idealized conditions: simulated detector noise, a single progenitor star, and a perfectly known bounce time. This paper removes those three idealizations. Signals are injected into real LIGO Hanford O4a noise, the training set is expanded to four progenitors (12–40 solar masses) with many rotation rates, and the bounce epoch is allowed to wander by as much as 20 ms. None of the three changes substantially lowers classification accuracy. The larger multi-progenitor data set actually raises it, reaching roughly 92 percent at high signal-to-noise ratio when the frequency-domain representation is used. The work therefore argues that EOS inference from core-collapse gravitational waves remains feasible under more realistic observing conditions.

Core claim

When gravitational-wave signals from rotating core bounce are classified by a linear support-vector machine, neither real O4a detector noise, nor progenitor diversity across 12–40 solar masses, nor bounce-time uncertainty of up to 20 ms significantly degrades accuracy relative to the idealized single-progenitor, simulated-noise case. The expanded data set instead improves training, so that time-domain accuracy at SNR = 200 rises from about 84 percent (single progenitor) to 91.6 ± 3.1 percent (all four progenitors).

What carries the argument

A linear-kernel support-vector machine trained on 30 ms windows of whitened, band-pass-filtered strain, using either the raw time series or its Fourier amplitude spectrum after the simulated waveform is injected into real or colored Gaussian noise at a controlled signal-to-noise ratio.

Load-bearing premise

The short axisymmetric waveforms around bounce (roughly -2 to +6 ms) already contain the EOS-dependent features the classifier needs, so that later multi-dimensional effects such as prompt convection can be neglected or only roughly included.

What would settle it

Repeat the identical classification pipeline on a large set of high-resolution three-dimensional simulations that include realistic prompt convection and anisotropic neutrino emission; if accuracy collapses once those later components enter the analysis window, the claimed robustness fails.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The manuscript tests whether three realism upgrades degrade machine-learning classification of four nuclear EOSs (SFHo, LS220, HSDD2, GShenFSU2.1) from rotating core-collapse supernova gravitational-wave signals. Using axisymmetric CoCoNuT waveforms for four progenitors (12–40 M⊙) spanning T/|W| ≈ 0.02–0.18 (886 total waveforms), the authors inject signals into real O4a LIGO Hanford noise and into simulated Gaussian noise colored by the detector PSD, allow bounce-time uncertainty Δtb up to 20 ms inside 30 ms analysis windows, and train a linear SVM (C=10) on time-domain and frequency-domain representations. Accuracy is reported as mean ± 1σ over 50 random train–test splits. Real and simulated noise give statistically consistent performance (e.g., ~91–92 % time-domain accuracy at SNR=200). Expanding from one to four progenitors raises accuracy when the dataset grows (Table I) but modestly lowers it when the training set is size-balanced (Table II). Frequency-domain features remain robust under bounce-time shifts while time-domain accuracy collapses; enlarging the frequency-domain training set by denser time shifts recovers ~91.6 % accuracy even at Δtb=20 ms. An appendix using longer Richers et al. waveforms that include prompt convection shows only a modest accuracy drop and confirms that the classifier still relies primarily on the bounce/ring-down segment.

Significance. If the reported robustness holds under the stated assumptions, the work supplies a concrete, controlled demonstration that real detector noise, multi-progenitor diversity, and realistic bounce-time uncertainty need not destroy discrete EOS classification for rapidly rotating CCSNe. The systematic real-versus-simulated noise comparison, the balanced-dataset control that isolates size from diversity, and the frequency-domain resilience under time shifts are useful methodological results for the growing literature on ML inference of nuclear physics from supernova GWs. The study is explicitly incremental—a step toward more realistic conditions—and correctly flags remaining limitations (axisymmetry, discrete EOS set, optimal orientation). These controlled experiments therefore constitute a solid intermediate contribution rather than a definitive inference pipeline.

major comments (2)
  1. Section II.A and Appendix A: the central claim of robustness rests on the assertion that the −2 to +6 ms bounce/ring-down window (or its approximate extension with unphysical 2-D convection) captures the EOS-dependent features the classifier learns. While the appendix test with Richers et al. waveforms is a useful control and shows only an ~11 % accuracy drop, the simulations remain axisymmetric and the convection is acknowledged to be poorly resolved. A clearer quantitative statement of how much the reported accuracies could change under high-resolution 3-D bounce-plus-convection signals is needed before the robustness claim can be taken as fully load-bearing for realistic Galactic events.
  2. Section III and Tables I–II: classification is performed on a fixed discrete set of four EOSs. The conclusion (Section IV) correctly notes that a continuous, parameterized EOS family and regression would be preferable, yet the abstract and main results still present high classification accuracies as evidence that EOS inference is robust. The manuscript should more explicitly bound the claim: the numbers demonstrate robustness of four-way classification under the tested systematics, not yet continuous EOS parameter recovery.
minor comments (4)
  1. Figure 3 caption: the quoted accuracies for real versus simulated noise appear swapped relative to the body text of Section III.A; please align caption and text.
  2. Section II.B: the precise definition of the 30 ms window placement relative to the random bounce-time offset Δtb could be stated more formally (e.g., injection start uniform on [0, Δtb]) so that the experiment is fully reproducible.
  3. Throughout: a few typographical slips remain (“asses”, “constrainghts”, “intoroduction” in the Tukey reference). A light copy-edit pass would clean them.
  4. Section II.C: the choice of linear SVM with C=10 is justified by prior work, but a one-sentence reminder of why a linear kernel remains adequate once real noise and multi-progenitor diversity are added would help readers who have not read the earlier papers.

Circularity Check

1 steps flagged

Empirical ML robustness study; prior self-citations supply baselines and method choice but do not force the reported accuracies by construction.

specific steps
  1. self citation load bearing [Introduction / Section II.C (method choice)]
    "In previous studies [75–77], we demonstrated the feasibility of using machine learning (ML) to infer the nuclear EOS from gravitational waves produced during rotating core bounce. ... To classify the EOS models using GW signals, we employ a support vector machine (SVM). This supervised ML algorithm demonstrated the best performance for similar tasks among the tested classical and deep learning models conducted by Abylkairov et al. [77]."

    The choice of SVM and the claim of prior promising accuracy rest on the authors' own earlier numerical experiments. This is ordinary self-citation of independent simulation results, not a definitional loop that forces the new multi-progenitor / real-noise accuracies. It is therefore only a minor, non-load-bearing circularity.

full rationale

The paper's central claims are measured classification accuracies under controlled relaxations of three assumptions (real O4a noise, multi-progenitor diversity, bounce-time uncertainty). These are obtained by training/evaluating an SVM on an expanded library of CoCoNuT waveforms injected into detector noise (Sections II–III, Tables I–II, Figures 3–4). No quantity is defined in terms of the target accuracy, no free parameter is fitted to the same data that is then called a prediction, and no uniqueness theorem is invoked. Self-citations to the authors' earlier works [75–77] establish the prior single-progenitor baseline and the choice of linear SVM; those earlier results are independent numerical experiments, not algebraic identities that make the present numbers inevitable. The balanced-dataset control (Table II) further isolates dataset-size effects from progenitor diversity, confirming that the improvement is empirical rather than definitional. Appendix A tests the effect of approximate prompt convection and finds only modest degradation, again an empirical measurement. Consequently the derivation chain is self-contained against its own simulation library; circularity is limited to ordinary, non-load-bearing self-citation of prior numerical work.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

The claim rests on standard numerical-relativity and ML practices plus a handful of domain modeling choices that are not independently verified inside the paper. No new physical entities are postulated; free parameters are ordinary hyper-parameters and analysis-window choices.

free parameters (3)
  • SVM regularization C = 10
    Fixed at C=10 after grid-search cross-validation; the reported accuracies depend on this choice.
  • analysis window length and placement = 30 ms / Δtb ≤ 20 ms
    30 ms windows with signal injected randomly up to Δtb; the 8 ms signal duration (−2 to +6 ms) is a modeling cut.
  • Tukey taper α and filter bands = α=0.1
    α=0.1, 20–2000 Hz bandpass, notches at 60/120/240 Hz; standard but still free analysis choices.
axioms (4)
  • domain assumption Axisymmetric CoCoNuT simulations with Ye(ρ) deleptonization and leakage/heating adequately capture the EOS-dependent bounce and early ring-down GW signal.
    Stated in Section II.A; justified by prior literature but not re-validated in 3-D here.
  • domain assumption The four chosen EOS models (SFHo, LS220, HSDD2, GShenFSU2.1) are sufficiently representative that classification accuracy among them generalizes to the broader EOS landscape.
    Section II.A; the paper notes that a continuous parameterized family would be preferable.
  • domain assumption Optimal source and detector orientations can be assumed without loss of the robustness conclusion.
    Explicitly listed as a remaining limitation in Section IV.
  • domain assumption Linear-kernel SVM is an adequate classifier for the task; more complex architectures would not reverse the robustness findings.
    Chosen because it performed best in the authors’ prior comparison (Abylkairov et al. 2024).

pith-pipeline@v1.1.0-grok45 · 24329 in / 2809 out tokens · 33815 ms · 2026-07-13T16:48:20.484916+00:00 · methodology

0 comments
read the original abstract

Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy. However, these analyses relied on several simplifying assumptions. In this work, we relax three key assumptions. First, we include real detector noise. Second, we expand the analysis from a single progenitor model to four models spanning 12 to 40 solar masses, and for each mass we consider multiple rotational configurations, from slow to rapid. Third, we introduce uncertainty in the core bounce time of up to 20 ms, rather than assuming it is known precisely. We find that none of these effects significantly degrades EOS classification performance. Instead, the larger dataset associated with multiple progenitor models and noise realizations improves training and classification accuracy. This study is a step in a broader effort to progressively incorporate more realistic conditions into gravitational-wave inference for core-collapse supernovae.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection

    gr-qc 2026-05 unverdicted novelty 7.0

    A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensi...

  2. The Generalization Gap in Machine Learning EoS Inference from Core-Collapse Supernova Gravitational Waves

    astro-ph.HE 2026-07 conditional novelty 6.5

    LightGBM and other regressors achieve R^{2}≈0.6–0.7 under random CV on CCSN GW catalogues but collapse to worse-than-mean performance under Leave-One-EoS-Out validation, exposing a generalisation gap for unseen EoS families.

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

    astro-ph.HE 2026-05 unverdicted novelty 5.0

    Machine learning extracts core rotation and signal properties from CCSN gravitational waves, with next-generation detectors constraining rotation beyond 100 kpc for favorable orientations despite some uncertainties.

Reference graph

Works this paper leans on

104 extracted references · 80 linked inside Pith · cited by 3 Pith papers

  1. [1]

    The LIGO Scientific Collaboration, the Virgo Col- laboration, the KAGRA Collaboration, A. G. Abac, I. Abouelfettouh, F. Acernese, and K. e. a. Ackley, GWTC-4.0: Updating the Gravitational-Wave Tran- sient Catalog with Observations from the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run, arXiv e-prints , arXiv:2508.18082 (2025), arXiv:2508.18082 [gr-qc]

    Pith/arXiv arXiv 2025

  2. [2]

    B. P. Abbott, R. Abbott, T. D. Abbott, S. Abraham, and F. Acernese (LIGO Scientific Collaboration and Virgo Collaboration and ASAS-SN Collaboration and DLT40 Collaboration), Optically targeted search for gravitational waves emitted by core-collapse supernovae during the first and second observing runs of advanced ligo and advanced virgo, Phys. Rev. D101, ...

    2020

  3. [3]

    M. J. Szczepa´ nczyk, Y. Zheng, J. M. Antelis, M. Ben- jamin, M.-A. Bizouard, A. Casallas-Lagos, P. Cerd´ a- Dur´ an, D. Davis, D. Gondek-Rosi´ nska, S. Klimenko, C. Moreno, M. Obergaulinger, J. Powell, D. Ramirez, B. Ratto, C. Richardson, A. Rijal, A. L. Stuver, P. Szewczyk, G. Vedovato, M. Zanolin, I. Bartos, S. Bhaumik, T. Bulik, M. Drago, J. A. Font, ...

    2024

  4. [4]

    Powell and B

    J. Powell and B. M¨ uller, Inferring astrophysical parame- ters of core-collapse supernovae from their gravitational- wave emission, Phys. Rev. D105, 063018 (2022), arXiv:2201.01397 [astro-ph.HE]

    Pith/arXiv arXiv 2022

  5. [5]

    S. E. Gossan, P. Sutton, A. Stuver, M. Zanolin, K. Gill, and C. D. Ott, Observing gravitational waves from core- collapse supernovae in the advanced detector era, Phys. Rev. D93, 042002 (2016), arXiv:1511.02836 [astro- ph.HE]

    Pith/arXiv arXiv 2016

  6. [6]

    M. J. Szczepa´ nczyk, J. M. Antelis, M. Benjamin, M. Cavagli` a, D. Gondek-Rosi´ nska, T. Hansen, S. Kli- menko, M. D. Morales, C. Moreno, S. Mukherjee, G. Nurbek, J. Powell, N. Singh, S. Sitmukhambetov, P. Szewczyk, O. Valdez, G. Vedovato, J. Westhouse, M. Zanolin, and Y. Zheng, Detecting and reconstructing gravitational waves from the next galactic core...

    Pith/arXiv arXiv 2021

  7. [7]

    Srivastava, S

    V. Srivastava, S. Ballmer, D. A. Brown, C. Afle, A. Bur- rows, D. Radice, and D. Vartanyan, Detection prospects of core-collapse supernovae with supernova-optimized third-generation gravitational-wave detectors, Phys. Rev. D100, 043026 (2019), arXiv:1906.00084 [gr-qc]

    Pith/arXiv arXiv 2019

  8. [8]

    Abdikamalov, G

    E. Abdikamalov, G. Pagliaroli, and D. Radice, Grav- itational Waves from Core-Collapse Supernovae, in Handbook of Gravitational Wave Astronomy, edited by C. Bambi, S. Katsanevas, and K. D. Kokkotas (2022) p. 21

    2022

  9. [9]

    Mezzacappa and M

    A. Mezzacappa and M. Zanolin, Gravitational Waves from Neutrino-Driven Core Collapse Supernovae: Pre- dictions, Detection, and Parameter Estimation, arXiv e-prints , arXiv:2401.11635 (2024), arXiv:2401.11635 [astro-ph.HE]

    Pith/arXiv arXiv 2024

  10. [10]

    B. M¨ uller, Core-Collapse Supernovae and their Grav- itational Wave Signals: The Status of Theory and Modeling, arXiv e-prints , arXiv:2603.24243 (2026), arXiv:2603.24243 [astro-ph.HE]

    arXiv 2026

  11. [11]

    Burrows and J

    A. Burrows and J. Goshy, A Theory of Supernova Ex- plosions, ApJ Lett.416, L75 (1993)

    1993

  12. [12]

    Janka, Conditions for shock revival by neutrino heating in core-collapse supernovae, A&A368, 527 (2001), arXiv:astro-ph/0008432 [astro-ph]

    H.-T. Janka, Conditions for shock revival by neutrino heating in core-collapse supernovae, A&A368, 527 (2001), arXiv:astro-ph/0008432 [astro-ph]

    Pith/arXiv arXiv 2001

  13. [13]

    M¨ uller, Hydrodynamics of core-collapse supernovae and their progenitors, Living Reviews in Computa- 9 tional Astrophysics6, 3 (2020), arXiv:2006.05083 [astro- ph.SR]

    B. M¨ uller, Hydrodynamics of core-collapse supernovae and their progenitors, Living Reviews in Computa- 9 tional Astrophysics6, 3 (2020), arXiv:2006.05083 [astro- ph.SR]

    Pith/arXiv arXiv 2020

  14. [14]

    Rampp and H.-T

    M. Rampp and H.-T. Janka, Spherically Symmetric Simulation with Boltzmann Neutrino Transport of Core Collapse and Postbounce Evolution of a 15 Msolar Star, ApJ Lett.539, L33 (2000), arXiv:astro-ph/0005438 [astro-ph]

    Pith/arXiv arXiv 2000

  15. [15]

    O’Connor and C

    E. O’Connor and C. D. Ott, The Progenitor Depen- dence of the Pre-explosion Neutrino Emission in Core- collapse Supernovae, Astrophys. J.762, 126 (2013), arXiv:1207.1100 [astro-ph.HE]

    Pith/arXiv arXiv 2013

  16. [16]

    Buras, H.-T

    R. Buras, H.-T. Janka, M. Rampp, and K. Kifoni- dis, Two-dimensional hydrodynamic core-collapse su- pernova simulations with spectral neutrino transport. II. Models for different progenitor stars, A&A457, 281 (2006), arXiv:astro-ph/0512189 [astro-ph]

    Pith/arXiv arXiv 2006

  17. [17]

    S. W. Bruenn, E. J. Lentz, W. R. Hix, A. Mezzacappa, J. A. Harris, O. E. B. Messer, E. Endeve, J. M. Blondin, M. A. Chertkow, E. J. Lingerfelt, P. Marronetti, and K. N. Yakunin, The Development of Explosions in Ax- isymmetric Ab Initio Core-collapse Supernova Simula- tions of 12-25 M Stars, Astrophys. J.818, 123 (2016), arXiv:1409.5779 [astro-ph.SR]

    Pith/arXiv arXiv 2016

  18. [18]

    Kotake, T

    K. Kotake, T. Takiwaki, T. Fischer, K. Nakamura, and G. Mart´ ınez-Pinedo, Impact of Neutrino Opacities on Core-collapse Supernova Simulations, Astrophys. J. 853, 170 (2018), arXiv:1801.02703 [astro-ph.HE]

    Pith/arXiv arXiv 2018

  19. [19]

    J. M. Blondin, A. Mezzacappa, and C. DeMarino, Sta- bility of Standing Accretion Shocks, with an Eye to- ward Core-Collapse Supernovae, Astrophys. J.584, 971 (2003), arXiv:astro-ph/0210634 [astro-ph]

    Pith/arXiv arXiv 2003

  20. [20]

    Foglizzo, L

    T. Foglizzo, L. Scheck, and H. T. Janka, Neutrino-driven Convection versus Advection in Core-Collapse Super- novae, Astrophys. J.652, 1436 (2006), arXiv:astro- ph/0507636 [astro-ph]

    arXiv 2006

  21. [21]

    M¨ uller, H.-T

    B. M¨ uller, H.-T. Janka, and A. Heger, New Two- dimensional Models of Supernova Explosions by the Neutrino-heating Mechanism: Evidence for Different Instability Regimes in Collapsing Stellar Cores, Astro- phys. J.761, 72 (2012), arXiv:1205.7078 [astro-ph.SR]

    Pith/arXiv arXiv 2012

  22. [22]

    S. M. Couch, E. Chatzopoulos, W. D. Arnett, and F. X. Timmes, The Three-dimensional Evolution to Core Col- lapse of a Massive Star, ApJ Lett.808, L21 (2015), arXiv:1503.02199 [astro-ph.HE]

    Pith/arXiv arXiv 2015

  23. [23]

    M¨ uller, T

    B. M¨ uller, T. Melson, A. Heger, and H.-T. Janka, Su- pernova simulations from a 3D progenitor model - Im- pact of perturbations and evolution of explosion proper- ties, MNRAS472, 491 (2017), arXiv:1705.00620 [astro- ph.SR]

    Pith/arXiv arXiv 2017

  24. [24]

    Kazeroni and E

    R. Kazeroni and E. Abdikamalov, The impact of pro- genitor asymmetries on the neutrino-driven convection in core-collapse supernovae, MNRAS494, 5360 (2020), arXiv:1911.08819 [astro-ph.SR]

    Pith/arXiv arXiv 2020

  25. [25]

    Telman, E

    Y. Telman, E. Abdikamalov, and T. Foglizzo, Convec- tive vortices in collapsing stars, MNRAS535, 1388 (2024), arXiv:2409.17737 [astro-ph.SR]

    Pith/arXiv arXiv 2024

  26. [26]

    Heger, S

    A. Heger, S. E. Woosley, and H. C. Spruit, Presupernova Evolution of Differentially Rotating Massive Stars In- cluding Magnetic Fields, Astrophys. J.626, 350 (2005), arXiv:astro-ph/0409422 [astro-ph]

    Pith/arXiv arXiv 2005

  27. [27]

    S. B. Popov and R. Turolla, Initial spin periods of neu- tron stars in supernova remnants, Astrophys. Space Sci. 341, 457 (2012), arXiv:1204.0632 [astro-ph.HE]

    Pith/arXiv arXiv 2012

  28. [28]

    Burrows, L

    A. Burrows, L. Dessart, E. Livne, C. D. Ott, and J. Murphy, Simulations of Magnetically Driven Super- nova and Hypernova Explosions in the Context of Rapid Rotation, Astrophys. J.664, 416 (2007), arXiv:astro- ph/0702539

    arXiv 2007

  29. [29]

    M¨ osta, S

    P. M¨ osta, S. Richers, C. D. Ott, R. Haas, A. L. Piro, K. Boydstun, E. Abdikamalov, C. Reisswig, and E. Schnetter, Magnetorotational Core-collapse Super- novae in Three Dimensions, ApJL785, L29 (2014), arXiv:1403.1230 [astro-ph.HE]

    Pith/arXiv arXiv 2014

  30. [30]

    Kuroda, A

    T. Kuroda, A. Arcones, T. Takiwaki, and K. Kotake, Magnetorotational Explosion of a Massive Star Sup- ported by Neutrino Heating in General Relativistic Three-dimensional Simulations, Astrophys. J.896, 102 (2020), arXiv:2003.02004 [astro-ph.HE]

    Pith/arXiv arXiv 2020

  31. [31]

    Obergaulinger and M

    M. Obergaulinger and M. ´A. Aloy, Magnetorota- tional core collapse of possible GRB progenitors - I. Explosion mechanisms, MNRAS492, 4613 (2020), arXiv:1909.01105 [astro-ph.HE]

    Pith/arXiv arXiv 2020

  32. [32]

    Bugli, J

    M. Bugli, J. Guilet, and M. Obergaulinger, Three- dimensional core-collapse supernovae with complex magnetic structures - I. Explosion dynamics, MNRAS 507, 443 (2021), arXiv:2105.00665 [astro-ph.HE]

    Pith/arXiv arXiv 2021

  33. [33]

    Eisenberg, O

    M. Eisenberg, O. Gottlieb, and E. Nakar, Observational signatures of stellar explosions driven by relativistic jets, MNRAS517, 582 (2022), arXiv:2201.08432 [astro- ph.HE]

    Pith/arXiv arXiv 2022

  34. [34]

    M. Pais, T. Piran, and E. Nakar, The velocity distri- bution of outflows driven by choked jets in stellar en- velopes, MNRAS519, 1941 (2023), arXiv:2208.14459 [astro-ph.HE]

    Pith/arXiv arXiv 1941

  35. [35]

    Takiwaki, K

    T. Takiwaki, K. Kotake, and Y. Suwa, Three- dimensional simulations of rapidly rotating core- collapse supernovae: finding a neutrino-powered explo- sion aided by non-axisymmetric flows, MNRAS461, L112 (2016), arXiv:1602.06759 [astro-ph.HE]

    Pith/arXiv arXiv 2016

  36. [36]

    Summa, H.-T

    A. Summa, H.-T. Janka, T. Melson, and A. Marek, Rotation-supported Neutrino-driven Supernova Explo- sions in Three Dimensions and the Critical Lu- minosity Condition, Astrophys. J.852, 28 (2018), arXiv:1708.04154 [astro-ph.HE]

    Pith/arXiv arXiv 2018

  37. [37]

    Abdikamalov, T

    E. Abdikamalov, T. Foglizzo, and O. Mukazhanov, Im- pact of rotation on the evolution of convective vortices in collapsing stars, MNRAS503, 3617 (2021)

    2021

  38. [38]

    A. C. Buellet, T. Foglizzo, J. Guilet, and E. Abdika- malov, Effect of stellar rotation on the development of post-shock instabilities during core-collapse super- novae, A&A674, A205 (2023), arXiv:2301.01962 [astro- ph.HE]

    Pith/arXiv arXiv 2023

  39. [39]

    Powell and B

    J. Powell and B. M¨ uller, The gravitational-wave emis- sion from the explosion of a 15 solar mass star with ro- tation and magnetic fields, MNRAS532, 4326 (2024), arXiv:2406.09691 [astro-ph.HE]

    Pith/arXiv arXiv 2024

  40. [40]

    J. W. Murphy, C. D. Ott, and A. Burrows, A Model for Gravitational Wave Emission from Neutrino-Driven Core-Collapse Supernovae, Astrophys. J.707, 1173 (2009), arXiv:0907.4762 [astro-ph.SR]

    Pith/arXiv arXiv 2009

  41. [41]

    Morozova, D

    V. Morozova, D. Radice, A. Burrows, and D. Var- tanyan, The Gravitational Wave Signal from Core- collapse Supernovae, Astrophys. J.861, 10 (2018), arXiv:1801.01914 [astro-ph.HE]

    Pith/arXiv arXiv 2018

  42. [42]

    C. D. Ott, E. Abdikamalov, P. M¨ osta, R. Haas, S. Drasco, E. P. O’Connor, C. Reisswig, C. A. Meakin, and E. Schnetter, General-relativistic Simula- 10 tions of Three-dimensional Core-collapse Supernovae, Astrophys. J.768, 115 (2013), arXiv:1210.6674 [astro- ph.HE]

    Pith/arXiv arXiv 2013

  43. [43]

    K. N. Yakunin, A. Mezzacappa, P. Marronetti, S. Yoshida, S. W. Bruenn, W. R. Hix, E. J. Lentz, O. E. Bronson Messer, J. A. Harris, E. Endeve, J. M. Blondin, and E. J. Lingerfelt, Gravitational wave signatures of ab initio two-dimensional core collapse supernova explosion models for 12 -25 M ⊙ stars, Phys. Rev. D92, 084040 (2015), arXiv:1505.05824 [astro-ph.HE]

    Pith/arXiv arXiv 2015

  44. [44]

    Kuroda, K

    T. Kuroda, K. Kotake, and T. Takiwaki, A New Gravitational-wave Signature from Standing Accretion Shock Instability in Supernovae, ApJ Lett.829, L14 (2016), arXiv:1605.09215 [astro-ph.HE]

    Pith/arXiv arXiv 2016

  45. [45]

    Torres-Forn´ e, P

    A. Torres-Forn´ e, P. Cerd´ a-Dur´ an, M. Obergaulinger, B. M¨ uller, and J. A. Font, Universal Relations for Gravitational-Wave Asteroseismology of Protoneu- tron Stars, Phys. Rev. Lett.123, 051102 (2019), arXiv:1902.10048 [gr-qc]

    Pith/arXiv arXiv 2019

  46. [46]

    Mezzacappa, P

    A. Mezzacappa, P. Marronetti, R. E. Landfield, E. J. Lentz, R. D. Murphy, W. Raphael Hix, J. A. Harris, S. W. Bruenn, J. M. Blondin, O. E. Bronson Messer, J. Casanova, and L. L. Kronzer, Core collapse super- nova gravitational wave emission for progenitors of 9.6, 15, and 25M⊙, Phys. Rev. D107, 043008 (2023), arXiv:2208.10643 [astro-ph.SR]

    Pith/arXiv arXiv 2023

  47. [47]

    Vartanyan, A

    D. Vartanyan, A. Burrows, T. Wang, M. S. B. Coleman, and C. J. White, Gravitational-wave signature of core- collapse supernovae, Phys. Rev. D107, 103015 (2023), arXiv:2302.07092 [astro-ph.HE]

    Pith/arXiv arXiv 2023

  48. [48]

    Sotani, B

    H. Sotani, B. M¨ uller, and T. Takiwaki, Universal- ity in supernova gravitational waves with protoneu- tron star properties, Phys. Rev. D109, 123021 (2024), arXiv:2405.09030 [astro-ph.HE]

    Pith/arXiv arXiv 2024

  49. [49]

    Ehring, S

    J. Ehring, S. Abbar, H.-T. Janka, G. Raffelt, K. Naka- mura, and K. Kotake, Gravitational-Wave Signatures of Nonstandard Neutrino Properties in Collapsing Stellar Cores, Phys. Rev. Lett.136, 021201 (2026), arXiv:2412.02750 [astro-ph.HE]

    arXiv 2026

  50. [50]

    Lella, G

    A. Lella, G. Lucente, D. Kresse, R. Glas, H.-T. Janka, and A. Mirizzi, Gravitational-Wave Signals for Supernova Explosions of Three-Dimensional Pro- genitors, arXiv e-prints , arXiv:2602.02651 (2026), arXiv:2602.02651 [astro-ph.HE]

    arXiv 2026

  51. [51]

    C. D. Ott, E. Abdikamalov, E. O’Connor, C. Reiss- wig, R. Haas, P. Kalmus, S. Drasco, A. Burrows, and E. Schnetter, Correlated gravitational wave and neu- trino signals from general-relativistic rapidly rotating iron core collapse, Phys. Rev. D86, 024026 (2012), arXiv:1204.0512 [astro-ph.HE]

    Pith/arXiv arXiv 2012

  52. [52]

    Fuller, H

    J. Fuller, H. Klion, E. Abdikamalov, and C. D. Ott, Supernova seismology: gravitational wave signatures of rapidly rotating core collapse, MNRAS450, 414 (2015), arXiv:1501.06951 [astro-ph.HE]

    Pith/arXiv arXiv 2015

  53. [53]

    Scheidegger, T

    S. Scheidegger, T. Fischer, S. C. Whitehouse, and M. Liebend¨ orfer, Gravitational waves from 3D MHD core collapse simulations, A&A490, 231 (2008), arXiv:0709.0168 [astro-ph]

    Pith/arXiv arXiv 2008

  54. [54]

    Shibagaki, T

    S. Shibagaki, T. Kuroda, K. Kotake, and T. Takiwaki, A new gravitational-wave signature of low-T/—W— in- stability in rapidly rotating stellar core collapse, MN- RAS493, L138 (2020), arXiv:1909.09730 [astro-ph.HE]

    Pith/arXiv arXiv 2020

  55. [55]

    Mueller and H

    E. Mueller and H. T. Janka, Gravitational radiation from convective instabilities in Type II supernova ex- plosions., A&A317, 140 (1997)

    1997

  56. [56]

    L. Choi, A. Burrows, and D. Vartanyan, Gravitational- wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae, Astrophys. J.975, 12 (2024), arXiv:2408.01525 [astro-ph.HE]

    Pith/arXiv arXiv 2024

  57. [57]

    M¨ uller, H.-T

    E. M¨ uller, H.-T. Janka, and A. Wongwathanarat, Parametrized 3D models of neutrino-driven super- nova explosions. Neutrino emission asymmetries and gravitational-wave signals, A&A537, A63 (2012), arXiv:1106.6301 [astro-ph.SR]

    Pith/arXiv arXiv 2012

  58. [58]

    Radice, V

    D. Radice, V. Morozova, A. Burrows, D. Vartanyan, and H. Nagakura, Characterizing the Gravitational Wave Signal from Core-collapse Supernovae, ApJL876, L9 (2019), arXiv:1812.07703 [astro-ph.HE]

    Pith/arXiv arXiv 2019

  59. [59]

    Birnholtz and T

    O. Birnholtz and T. Piran, Gravitational wave memory from gamma ray bursts’ jets, Phys. Rev. D87, 123007 (2013), arXiv:1302.5713 [astro-ph.HE]

    Pith/arXiv arXiv 2013

  60. [60]

    Gottlieb, H

    O. Gottlieb, H. Nagakura, A. Tchekhovskoy, P. Natara- jan, E. Ramirez-Ruiz, S. Banagiri, J. Jacquemin-Ide, N. Kaaz, and V. Kalogera, Jetted and Turbulent Stel- lar Deaths: New LVK-detectable Gravitational-wave Sources, ApJ Lett.951, L30 (2023), arXiv:2209.09256 [astro-ph.HE]

    Pith/arXiv arXiv 2023

  61. [61]

    Cusinato, M

    M. Cusinato, M. Obergaulinger, M. ´A. Aloy, and J. A. Font, Resonant amplification of multimessenger emis- sion in rotating stellar core collapse, Physical Review Research8, 013180 (2026)

    2026

  62. [62]

    Abdikamalov, S

    E. Abdikamalov, S. Gossan, A. M. DeMaio, and C. D. Ott, Measuring the angular momentum distribution in core-collapse supernova progenitors with gravitational waves, Phys. Rev. D90, 044001 (2014), arXiv:1311.3678 [astro-ph.SR]

    Pith/arXiv arXiv 2014

  63. [63]

    M. A. Pajkos, S. M. Couch, K.-C. Pan, and E. P. O’Connor, Features of Accretion-phase Gravitational- wave Emission from Two-dimensional Rotating Core- collapse Supernovae, Astrophys. J.878, 13 (2019), arXiv:1901.09055 [astro-ph.HE]

    Pith/arXiv arXiv 2019

  64. [64]

    Logue, C

    J. Logue, C. D. Ott, I. S. Heng, P. Kalmus, and J. H. C. Scargill, Inferring core-collapse supernova physics with gravitational waves, Phys. Rev. D86, 044023 (2012), arXiv:1202.3256 [gr-qc]

    Pith/arXiv arXiv 2012

  65. [65]

    Powell, A

    J. Powell, A. Iess, M. Llorens-Monteagudo, M. Ober- gaulinger, B. M¨ uller, A. Torres-Forn´ e, E. Cuoco, and J. A. Font, Determining the core-collapse super- nova explosion mechanism with current and future gravitational-wave observatories, Phys. Rev. D109, 063019 (2024), arXiv:2311.18221 [astro-ph.HE]

    Pith/arXiv arXiv 2024

  66. [66]

    Bizouard, P

    M.-A. Bizouard, P. Maturana-Russel, A. Torres-Forn´ e, M. Obergaulinger, P. Cerd´ a-Dur´ an, N. Christensen, J. A. Font, and R. Meyer, Inference of protoneutron star properties from gravitational-wave data in core- collapse supernovae, Phys. Rev. D103, 063006 (2021), arXiv:2012.00846 [gr-qc]

    Pith/arXiv arXiv 2021

  67. [67]

    Sotani, T

    H. Sotani, T. Takiwaki, and H. Togashi, Universal re- lation for supernova gravitational waves, Phys. Rev. D 104, 123009 (2021), arXiv:2110.03131 [astro-ph.HE]

    Pith/arXiv arXiv 2021

  68. [68]

    Bruel, M.-A

    T. Bruel, M.-A. Bizouard, M. Obergaulinger, P. Maturana-Russel, A. Torres-Forn´ e, P. Cerd´ a-Dur´ an, N. Christensen, J. A. Font, and R. Meyer, Inference of protoneutron star properties in core-collapse su- pernovae from a gravitational-wave detector network, Phys. Rev. D107, 083029 (2023), arXiv:2301.10019 [astro-ph.HE]. 11

    Pith/arXiv arXiv 2023

  69. [69]

    Casallas-Lagos, J

    A. Casallas-Lagos, J. M. Antelis, C. Moreno, M. Zano- lin, A. Mezzacappa, and M. J. Szczepa´ nczyk, Charac- terizing the temporal evolution of the high-frequency gravitational wave emission for a core collapse super- nova with laser interferometric data: A neural network approach, Phys. Rev. D108, 084027 (2023)

    2023

  70. [70]

    Richers, C

    S. Richers, C. D. Ott, E. Abdikamalov, E. O’Connor, and C. Sullivan, Equation of state effects on gravita- tional waves from rotating core collapse, Phys. Rev. D 95, 063019 (2017), arXiv:1701.02752 [astro-ph.HE]

    Pith/arXiv arXiv 2017

  71. [71]

    M. C. Edwards, Classifying the equation of state from rotating core collapse gravitational waves with deep learning, Phys. Rev. D103, 024025 (2021), arXiv:2009.07367 [astro-ph.IM]

    Pith/arXiv arXiv 2021

  72. [72]

    Chao, C.-Z

    Y.-S. Chao, C.-Z. Su, T.-Y. Chen, D.-W. Wang, and K.-C. Pan, Determining the Core Structure and Nuclear Equation of State of Rotating Core-collapse Supernovae with Gravitational Waves by Convolutional Neural Net- works, Astrophys. J.939, 13 (2022), arXiv:2209.10089 [astro-ph.HE]

    Pith/arXiv arXiv 2022

  73. [73]

    N. E. Wolfe, C. Fr¨ ohlich, J. M. Miller, A. Torres- Forn´ e, and P. Cerd´ a-Dur´ an, Gravitational Wave Eigen- frequencies from Neutrino-driven Core-collapse Super- novae, Astrophys. J.954, 161 (2023), arXiv:2303.16962 [astro-ph.HE]

    Pith/arXiv arXiv 2023

  74. [74]

    R. D. Murphy, A. Casallas-Lagos, A. Mezzacappa, M. Zanolin, R. E. Landfield, E. J. Lentz, P. Marronetti, J. M. Antelis, and C. Moreno, Dependence of the re- constructed core-collapse supernova gravitational wave high-frequency feature on the nuclear equation of state in real interferometric data, Phys. Rev. D110, 083006 (2024), arXiv:2406.01784 [astro-ph.HE]

    Pith/arXiv arXiv 2024

  75. [75]

    Mitra, D

    A. Mitra, D. Orel, Y. S. Abylkairov, B. Shukirgaliyev, and E. Abdikamalov, Probing nuclear physics with su- pernova gravitational waves and machine learning, MN- RAS529, 3582 (2024), arXiv:2310.15649 [astro-ph.HE]

    Pith/arXiv arXiv 2024

  76. [76]

    Y. S. Abylkairov, M. C. Edwards, D. Orel, A. Mitra, B. Shukirgaliyev, and E. Abdikamalov, Evaluating ma- chine learning models for supernova gravitational wave signal classification, Machine Learning: Science and Technology5, 045077 (2024), arXiv:2409.14508 [astro- ph.HE]

    Pith/arXiv arXiv 2024

  77. [77]

    Sultan Abylkairov, M

    Y. Sultan Abylkairov, M. C. Edwards, A. Ostrikov, Y. Tleukhanov, A. Torres-Forn´ e, P. Cerd´ a-Dur´ an, J. A. Font, M. J. Szczepa´ nczyk, and E. Abdikamalov, As- sessing the distance for probing the nuclear equation of state with supernova gravitational waves, Phys. Rev. D , (2025)

    2025

  78. [78]

    A. G. Abac, I. Abouelfettouh, F. Acernese, K. Ack- ley, S. Adhicary, D. Adhikari, N. Adhikari, R. X. Ad- hikari, V. K. Adkins, S. Afroz, D. Agarwal, M. Agathos, M. Aghaei Abchouyeh, O. D. Aguiar, S. Ahmadzadeh, L. Aiello, A. Ain, P. Ajith, S. Akcay, T. Akutsu, S. Al- banesi, R. A. Alfaidi, A. Al-Jodah, C. All´ en´ e, A. Al- locca, S. Al-Shammari, P. A. Al...

    Pith/arXiv arXiv 2025

  79. [79]

    S. J. Smartt, Progenitors of Core-Collapse Super- novae, Annu. Rev. Astron. Astrophys.47, 63 (2009), arXiv:0908.0700 [astro-ph.SR]

    Pith/arXiv arXiv 2009

  80. [80]

    Sukhbold, S

    T. Sukhbold, S. E. Woosley, and A. Heger, A High- resolution Study of Presupernova Core Structure, As- trophys. J.860, 93 (2018), arXiv:1710.03243 [astro- ph.HE]

    Pith/arXiv arXiv 2018

Showing first 80 references.