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Causal neural networks reconstruct fast-rotating neutron star observables thousands of times faster than traditional solvers.

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2026-05-10 20:04 UTC

load-bearing objection The paper trains causal CNNs on RNS data to speed up rotating neutron star calculations, but only validates on three EoS with no error metrics shown. the 2 major comments →

arxiv 2604.05428 v1 submitted 2026-04-07 astro-ph.HE gr-qc

Reconstruction of fast-rotating neutron star observables with the neural network

Wen Liu , Lingxiao Wang , Zhenyu Zhu This is my paper
classification astro-ph.HE gr-qc
keywords neutron starsequation of stateneural networksrotating neutron starsastrophysical inferencecausal networkscomputational astrophysicsrapid rotation
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.

This paper develops causal convolutional neural networks to rapidly compute neutron star properties from an equation of state for static, Keplerian, and rotating cases. Traditional RNS calculations take around 30 minutes per configuration, which blocks statistical inference that needs thousands of evaluations. The networks are trained on RNS-generated data and validated on three equations of state, reproducing the original results to high accuracy. Evaluation time drops to about 50 milliseconds per case while the network architecture maintains the physical ordering of dependence on the equation of state. The speedup directly supports efficient inference analyses on the interiors of rapidly spinning neutron stars.

Core claim

The authors train causal convolutional neural networks on datasets produced by the RNS code to reconstruct neutron star observables. These networks preserve the chronological-like dependence of properties on the equation of state and deliver accurate results for static, Keplerian, and rotating configurations. Validation on the SFHo, SLy4, and DD2 equations of state confirms close agreement with direct RNS output. The trained models evaluate a full configuration for one equation of state in roughly 50 milliseconds, compared with typical RNS runtimes of 30 minutes, thereby enabling large-scale inference studies that involve rapidly rotating neutron stars.

What carries the argument

Causal convolutional neural networks that enforce chronological-like dependence of neutron star observables on the equation of state.

Load-bearing premise

Networks trained on a finite collection of equations of state and rotation rates will generalize accurately to arbitrary equations of state and rotation rates while preserving physical causal relations between inputs and outputs.

What would settle it

Compute network predictions for an equation of state withheld from training, at a high rotation rate near the mass-shedding limit, and compare the full set of output observables against independent RNS runs for the same central density and angular velocity.

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 / 1 minor

Summary. The manuscript develops causal convolutional neural networks trained on RNS-generated datasets to reconstruct neutron-star observables (including for static, Keplerian, and rotating configurations) directly from the equation of state. It reports that the networks reproduce RNS results for the three EoS SFHo, SLy4, and DD2, with per-EoS evaluation times of ~50 ms versus ~30 min for direct RNS integration, thereby enabling efficient inference analyses involving rapidly rotating neutron stars.

Significance. If the reported accuracy and generalization hold, the work would supply a practical surrogate model that removes the computational bottleneck of 2D rotating-NS structure calculations, allowing Bayesian inference or population studies that require thousands of model evaluations to become feasible within current resources.

major comments (2)
  1. [Abstract / Results] Abstract and Results section: the claim that the networks 'accurately reproduce the RNS results' on SFHo, SLy4, and DD2 is unsupported by any quantitative error metrics (maximum or median relative errors on equatorial radius, moment of inertia, or Kepler frequency), training-set size, or explicit statement of whether these three EoS were held out from training.
  2. [Methods / Validation] Methods and Validation sections: no information is given on the diversity or number of EoS used to generate the training set, the distribution of rotation rates (especially near the Kepler limit), or any test of generalization to EoS stiffness or spin values outside the three validation cases; without these the central claim that the networks enable inference for arbitrary EoS remains unproven.
minor comments (1)
  1. [Abstract] Abstract contains a typographical error: 'the they accurately' should read 'they accurately'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive comments, which identify important omissions that will improve the manuscript. We address each point below and will revise the paper to incorporate the requested quantitative details and dataset information.

read point-by-point responses

  1. Referee: [Abstract / Results] Abstract and Results section: the claim that the networks 'accurately reproduce the RNS results' on SFHo, SLy4, and DD2 is unsupported by any quantitative error metrics (maximum or median relative errors on equatorial radius, moment of inertia, or Kepler frequency), training-set size, or explicit statement of whether these three EoS were held out from training.

    Authors: We agree that quantitative error metrics are needed to support the accuracy statement. In the revised manuscript we will add explicit values for the maximum and median relative errors on equatorial radius, moment of inertia, and Kepler frequency for SFHo, SLy4, and DD2. We will also report the training-set size and state that these three EoS were excluded from training and used exclusively for validation. revision: yes

  2. Referee: [Methods / Validation] Methods and Validation sections: no information is given on the diversity or number of EoS used to generate the training set, the distribution of rotation rates (especially near the Kepler limit), or any test of generalization to EoS stiffness or spin values outside the three validation cases; without these the central claim that the networks enable inference for arbitrary EoS remains unproven.

    Authors: We acknowledge that the current text lacks sufficient detail on the training data and generalization tests. The training set was generated from a broad collection of EoS spanning a range of stiffnesses, with rotation rates sampled up to the Kepler limit. In the revision we will specify the number of EoS employed, describe the rotation-rate distribution, and present additional validation results on EoS stiffnesses and spin values outside the three reported cases. These additions will directly support the applicability to arbitrary EoS in inference studies. revision: yes

Circularity Check

0 steps flagged

No circularity: standard supervised surrogate on external RNS simulator

full rationale

The paper generates training data with the independent RNS code, trains a causal CNN to map EoS inputs to NS observables, and validates reproduction on three EoS. This is ordinary supervised regression with no derivation, ansatz, or prediction that reduces to its own fitted inputs by construction. No self-citations are load-bearing for the central claim, and the speedup follows directly from NN inference speed versus RNS runtime. The result is self-contained against the external benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the empirical performance of a neural network trained to emulate RNS output; no new physical axioms or invented entities are introduced beyond the standard assumptions already present in the RNS code and the chosen equations of state.

pith-pipeline@v0.9.0 · 5466 in / 1238 out tokens · 91264 ms · 2026-05-10T20:04:24.001028+00:00 · methodology

0 comments
read the original abstract

Rotation can significantly affect neutron-star (NS) properties, but accurate modeling of rapidly rotating NSs requires solving a two-dimensional, axially symmetric system, making traditional calculations too expensive for inference analyses that demand a large amount of model evaluations. We develop a causal convolutional neural networks that preserve the chronological-like dependence of NS properties on the equation of state (EoS) and rapidly reconstruct observables for static, Keplerian, and rotating configurations. Using \texttt{RNS}, we generate a dataset of NS observables and use it to train our networks. We validate our networks with three representative EoS (SFHo, SLy4, and DD2) and find that the they accurately reproduce the \texttt{RNS} results. The trained networks evaluate NS configurations for a single EoS in $\sim 50$ms, providing a substantial speedup over typical \texttt{RNS} runtimes of $\sim 30$ min and enabling efficient inference analyses involving rapidly rotating NSs.

Figures

Figures reproduced from arXiv: 2604.05428 by Lingxiao Wang, Wen Liu, Zhenyu Zhu.

Figure 1
Figure 1. Figure 1: The structure of causal convolutional networks for rotating configurations. For the other two networks (static and Keplerian), the structures are slightly different: they take only the one-array EoS input and use fewer (5) hidden layers and fewer (64) channels. Note that the different colored arrows in latent layes illustrate the causal receptive field of individual nodes at different sequence position. Pl… view at source ↗
Figure 2
Figure 2. Figure 2: The parity plots for the Keplerian-rotation network compare the ground truth and the predictions, shown on the horizontal and vertical axes, respectively. The data points in the training and test sets are denoted by blue and orange dots. The identity lines are shown as black dashed lines. The results for gravitational mass, equatorial radius and angular velocity are shown in the left, middle and right pane… view at source ↗
Figure 3
Figure 3. Figure 3: The same parity plots as in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The mass-radius relations of rotating NSs evaluated at the baseline axes ratios for SFHo (left), SLy4 (middle), and DD2 (right) EoS. The RNS ground truth and NN predictions are shown as lines and dots, respectively. Colors indicate different axes ratios, as well as the static and Kepler cases. been trained to sufficient accuracy to reconstruct the properties of rotating NSs. Therefore, we did not remove th… view at source ↗
Figure 5
Figure 5. Figure 5: The angular velocity as a function of mass for different EoS. The labels are the same as in the previous figures. baseline axes ratios. We then combine these results with the static and Kepler-rotating models and use interpolation to obtain the observables at intermediate rotation states (i.e., other values of rp/re). In [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The mass-radius relations for configurations with five values of Ω (0.2, 0.4, 0.6, 0.8, 1.0, in units of 104 s −1 ). The static and Kepler models are also included, and the labels for them, as well as for the RNS ground truth and the NN predictions, are the same as in previous figures. errors of NN prediction remain at a high level of precision. This conclusion also holds for the SLy4 EoS. Therefore, our i… view at source ↗
Figure 7
Figure 7. Figure 7: Other observables as a function of gravitational mass M or radius R for SFHo EoS. The labels are the same as in [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Foundation/ArrowOfTime.lean arrow_from_z echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    we adopt the causal convolutional neural network ... which retains this chronological-like feature of the system, to learn the RNS solver. ... the output at a given position depends only on earlier elements of the input sequence, and thus preserves the causality of the sequence.

  • IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    the EoS dependence of NS properties is analogous to that of causal time-series data

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

90 extracted references · 90 canonical work pages · 4 internal anchors

  1. [1]

    Physical Review Letters , archivePrefix = "arXiv", eprint =

    Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017, Phys. Rev. Lett., 119, 161101, doi: 10.1103/PhysRevLett.119.161101 —. 2018, Physical Review Letters, 121, 161101, doi: 10.1103/PhysRevLett.121.161101 —. 2019, Physical Review X, 9, 011001, doi: 10.1103/PhysRevX.9.011001 —. 2020, ApJL, 892, L3, doi: 10.3847/2041-8213/ab75f5

    work page doi:10.1103/physrevlett.119.161101 2017

  2. [2]

    On the Sound Speed in Neutron Stars.The As- trophysical Journal Letters, 939(2):L34, November 2022

    Altiparmak, S., Ecker, C., & Rezzolla, L. 2022, ApJL, 939, L34, doi: 10.3847/2041-8213/ac9b2a

    work page doi:10.3847/2041-8213/ac9b2a 2022

  3. [3]

    Andersson, N., & Comer, G. L. 2001, Classical and Quantum Gravity, 18, 969, doi: 10.1088/0264-9381/18/6/302

    work page doi:10.1088/0264-9381/18/6/302 2001

  4. [4]

    2018, Gravitational-Wave Constraints on the Neutron-Star-Matter Equation of State, Phys

    Annala, E., Gorda, T., Kurkela, A., & Vuorinen, A. 2018, PhRvL, 120, 172703, doi: 10.1103/PhysRevLett.120.172703

    work page doi:10.1103/physrevlett.120.172703 2018

  5. [5]

    G., Pleunis, Z., Hessels, J

    Bassa, C. G., Pleunis, Z., Hessels, J. W. T., et al. 2017, ApJL, 846, L20, doi: 10.3847/2041-8213/aa8400

    work page doi:10.3847/2041-8213/aa8400 2017

  6. [6]

    1971, Astrophys

    Baym, G., Pethick, C., & Sutherland, P. 1971, ApJ, 170, 299, doi: 10.1086/151216

    work page doi:10.1086/151216 1971

  7. [7]

    1998, PhRvD, 58, 104020, doi: 10.1103/PhysRevD.58.104020

    Bonazzola, S., Gourgoulhon, E., & Marck, J.-A. 1998, PhRvD, 58, 104020, doi: 10.1103/PhysRevD.58.104020

    work page doi:10.1103/physrevd.58.104020 1998

  8. [8]

    , keywords =

    Breschi, M., Perego, A., Bernuzzi, S., et al. 2021, MNRAS, 505, 1661, doi: 10.1093/mnras/stab1287

    work page doi:10.1093/mnras/stab1287 2021

  9. [9]

    A Skyrme parametrization from subnuclear to neutron star densitiesPart II. Nuclei far from stabilities

    Schaeffer, R. 1998, NuPhA, 635, 231, doi: 10.1016/S0375-9474(98)00180-8

    work page doi:10.1016/s0375-9474(98)00180-8 1998

  10. [10]

    2024, Astrophys

    Choudhury, D., Salmi, T., Vinciguerra, S., et al. 2024, ApJL, 971, L20, doi: 10.3847/2041-8213/ad5a6f

    work page doi:10.3847/2041-8213/ad5a6f 2024

  11. [11]

    B., Shapiro, S

    Cook, G. B., Shapiro, S. L., & Teukolsky, S. A. 1994, ApJ, 422, 227, doi: 10.1086/173721

    work page doi:10.1086/173721 1994

  12. [12]

    W., Dietrich, T., Margalit, B., & Metzger, B

    Coughlin, M. W., Dietrich, T., Margalit, B., & Metzger, B. D. 2019, MNRAS, 489, L91, doi: 10.1093/mnrasl/slz133

    work page doi:10.1093/mnrasl/slz133 2019

  13. [13]

    2019 a , , 100, 044003, 10.1103/PhysRevD.100.044003

    Dietrich, T., Samajdar, A., Khan, S., et al. 2019, PhRvD, 100, 044003, doi: 10.1103/PhysRevD.100.044003

    work page doi:10.1103/physrevd.100.044003 2019

  14. [14]

    2025, PhRvD, 112, 103023, doi: 10.1103/krc7-kz2l Flanagan, ´E

    Finch, E., Legred, I., Chatziioannou, K., et al. 2025, PhRvD, 112, 103023, doi: 10.1103/krc7-kz2l Flanagan, ´E. ´E., & Hinderer, T. 2008, PhRvD, 77, 021502, doi: 10.1103/PhysRevD.77.021502

    work page doi:10.1103/krc7-kz2l 2025

  15. [15]

    T., Pennucci, T

    Fonseca, E., Cromartie, H. T., Pennucci, T. T., et al. 2021, ApJL, 915, L12, doi: 10.3847/2041-8213/ac03b8

    work page doi:10.3847/2041-8213/ac03b8 2021

  16. [16]

    2018, PhRvD, 98, 023019, doi: 10.1103/PhysRevD.98.023019 —

    Fujimoto, Y., Fukushima, K., & Murase, K. 2018, PhRvD, 98, 023019, doi: 10.1103/PhysRevD.98.023019 —. 2020, PhRvD, 101, 054016, doi: 10.1103/PhysRevD.101.054016

    work page doi:10.1103/physrevd.98.023019 2018

  17. [17]

    2022, PhRvD, 106, 123529, doi: 10.1103/PhysRevD.106.123529

    Ghosh, T., Biswas, B., & Bose, S. 2022, PhRvD, 106, 123529, doi: 10.1103/PhysRevD.106.123529

    work page doi:10.1103/physrevd.106.123529 2022

  18. [18]

    1999, A&A, 349, 851, doi: 10.48550/arXiv.astro-ph/9907225

    Gourgoulhon, E., Haensel, P., Livine, R., et al. 1999, A&A, 349, 851, doi: 10.48550/arXiv.astro-ph/9907225

    work page internal anchor Pith review doi:10.48550/arxiv.astro-ph/9907225 1999

  19. [19]

    Determining the equation of state of neutron stars with Einstein Telescope using tidal effects and r-mode excitations from a population of binary inspirals,

    Gupta, P. K., Puecher, A., Pang, P. T. H., et al. 2022, arXiv e-prints, arXiv:2205.01182, doi: 10.48550/arXiv.2205.01182

    work page doi:10.48550/arxiv.2205.01182 2022

  20. [20]

    Plausible presence of new state in neutron stars with masses above 0.98MTOV

    Han, M.-Z., Huang, Y.-J., Tang, S.-P., & Fan, Y.-Z. 2023a, Science Bulletin, 68, 913, doi: 10.1016/j.scib.2023.04.007

    work page doi:10.1016/j.scib.2023.04.007 2023

  21. [21]

    2021, ApJ, 919, 11, doi: 10.3847/1538-4357/ac11f8

    Han, M.-Z., Jiang, J.-L., Tang, S.-P., & Fan, Y.-Z. 2021, ApJ, 919, 11, doi: 10.3847/1538-4357/ac11f8

    work page doi:10.3847/1538-4357/ac11f8 2021

  22. [22]

    2023b, ApJ, 950, 77, doi: 10.3847/1538-4357/acd050

    Han, M.-Z., Tang, S.-P., & Fan, Y.-Z. 2023b, ApJ, 950, 77, doi: 10.3847/1538-4357/acd050

    work page doi:10.3847/1538-4357/acd050

  23. [23]

    Hessels, J. W. T., Ransom, S. M., Stairs, I. H., et al. 2006, Science, 311, 1901, doi: 10.1126/science.1123430

    work page doi:10.1126/science.1123430 2006

  24. [24]

    2008, Astrophys

    Hinderer, T. 2008, ApJ, 677, 1216, doi: 10.1086/533487

    work page doi:10.1086/533487 2008

  25. [25]

    D., Lang, R

    Hinderer, T., Lackey, B. D., Lang, R. N., & Read, J. S. 2010, PhRvD, 81, 123016, doi: 10.1103/PhysRevD.81.123016

    work page doi:10.1103/physrevd.81.123016 2010

  26. [26]

    2022, ApJ, 926, 196, doi: 10.3847/1538-4357/ac490e

    Belczynski, K. 2022, ApJ, 926, 196, doi: 10.3847/1538-4357/ac490e

    work page doi:10.3847/1538-4357/ac490e 2022

  27. [27]

    J., & Kehl, M

    Hu, H., Kramer, M., Wex, N., Champion, D. J., & Kehl, M. S. 2020, MNRAS, 497, 3118, doi: 10.1093/mnras/staa2107

    work page doi:10.1093/mnras/staa2107 2020

  28. [28]

    Huxford, R

    Huxford, R., Kashyap, R., Borhanian, S., et al. 2024, PhRvD, 109, 103035, doi: 10.1103/PhysRevD.109.103035

    work page doi:10.1103/physrevd.109.103035 2024

  29. [29]

    Forecasting the Detection Capabilities of Third-generation Gravitational-wave Detectors Using GWFAST

    Iacovelli, F., Mancarella, M., Foffa, S., & Maggiore, M. 2022, ApJ, 941, 208, doi: 10.3847/1538-4357/ac9cd4

    work page doi:10.3847/1538-4357/ac9cd4 2022

  30. [30]

    Iacovelli, M

    Iacovelli, F., Mancarella, M., Mondal, C., et al. 2023, PhRvD, 108, 122006, doi: 10.1103/PhysRevD.108.122006

    work page doi:10.1103/physrevd.108.122006 2023

  31. [31]

    1989, MNRAS, 237, 355, doi: 10.1093/mnras/237.2.355

    Komatsu, H., Eriguchi, Y., & Hachisu, I. 1989, MNRAS, 237, 355, doi: 10.1093/mnras/237.2.355

    work page doi:10.1093/mnras/237.2.355 1989

  32. [32]

    Physical Review X , keywords =

    Kramer, M., Stairs, I. H., Manchester, R. N., et al. 2021, Physical Review X, 11, 041050, doi: 10.1103/PhysRevX.11.041050

    work page doi:10.1103/physrevx.11.041050 2021

  33. [33]

    2025, PhRvD, 112, 063003, doi: 10.1103/9kh9-xfpd

    Chatziioannou, K. 2025, PhRvD, 112, 063003, doi: 10.1103/9kh9-xfpd

    work page doi:10.1103/9kh9-xfpd 2025

  34. [34]

    O., Chatziioannou, K., & Essick, R

    Legred, I., Sy-Garcia, B. O., Chatziioannou, K., & Essick, R. 2024, PhRvD, 109, 023020, doi: 10.1103/PhysRevD.109.023020

    work page doi:10.1103/physrevd.109.023020 2024

  35. [35]

    2016, PhRvD, 94, 083010, doi: 10.1103/PhysRevD.94.083010

    Li, A., Zhang, B., Zhang, N.-B., et al. 2016, PhRvD, 94, 083010, doi: 10.1103/PhysRevD.94.083010

    work page doi:10.1103/physrevd.94.083010 2016

  36. [36]

    2017, ApJ, 844, 41, doi: 10.3847/1538-4357/aa7a00

    Li, A., Zhu, Z.-Y., & Zhou, X. 2017, ApJ, 844, 41, doi: 10.3847/1538-4357/aa7a00

    work page doi:10.3847/1538-4357/aa7a00 2017

  37. [37]

    2024, PhRvD, 110, 103040, doi: 10.1103/PhysRevD.110.103040

    Li, B.-A., Grundler, X., Xie, W.-J., & Zhang, N.-B. 2024, PhRvD, 110, 103040, doi: 10.1103/PhysRevD.110.103040

    work page doi:10.1103/physrevd.110.103040 2024

  38. [38]

    2025, Phys

    Li, R., Han, S., Lin, Z., et al. 2025, PhRvD, 111, 074026, doi: 10.1103/PhysRevD.111.074026 10

    work page doi:10.1103/physrevd.111.074026 2025

  39. [39]

    2010, Phys

    Lindblom, L. 2010, PhRvD, 82, 103011, doi: 10.1103/PhysRevD.82.103011 —. 2022, PhRvD, 105, 063031, doi: 10.1103/PhysRevD.105.063031

    work page doi:10.1103/physrevd.82.103011 2010

  40. [40]

    2024, PhRvD, 110, 083030, doi: 10.1103/PhysRevD.110.083030

    Lindblom, L., & Zhou, T. 2024, PhRvD, 110, 083030, doi: 10.1103/PhysRevD.110.083030

    work page doi:10.1103/physrevd.110.083030 2024

  41. [41]

    2020, JCAP, 03, 050, doi: 10.1088/1475-7516/2020/03/050

    Maggiore, M., Van Den Broeck, C., Bartolo, N., et al. 2020, JCAP, 2020, 050, doi: 10.1088/1475-7516/2020/03/050

    work page doi:10.1088/1475-7516/2020/03/050 2020

  42. [42]

    Margalit, B., & Metzger, B. D. 2017, ApJL, 850, L19, doi: 10.3847/2041-8213/aa991c

    work page doi:10.3847/2041-8213/aa991c 2017

  43. [43]

    2025, arXiv e-prints, arXiv:2508.08750, doi: 10.48550/arXiv.2508.08750

    Markin, I., Puecher, A., Bulla, M., & Dietrich, T. 2025, arXiv e-prints, arXiv:2508.08750, doi: 10.48550/arXiv.2508.08750

    work page doi:10.48550/arxiv.2508.08750 2025

  44. [44]

    C., Lamb, F

    Miller, M. C., Lamb, F. K., Dittmann, A. J., et al. 2019, Astrophys. J. Lett., 887, L24, doi: 10.3847/2041-8213/ab50c5 —. 2021, Astrophys. J. Lett., 918, L28, doi: 10.3847/2041-8213/ac089b

    work page doi:10.3847/2041-8213/ab50c5 2019

  45. [45]

    2020, A&A, 642, A78, doi: 10.1051/0004-6361/202038130

    Morawski, F., & Bejger, M. 2020, A&A, 642, A78, doi: 10.1051/0004-6361/202038130

    work page doi:10.1051/0004-6361/202038130 2020

  46. [46]

    Most, Lukas R

    Most, E. R., Weih, L. R., Rezzolla, L., & Schaffner-Bielich, J. 2018, PhRvL, 120, 261103, doi: 10.1103/PhysRevLett.120.261103

    work page doi:10.1103/physrevlett.120.261103 2018

  47. [47]

    2024, ApJ, 962, 61, doi: 10.3847/1538-4357/ad1758

    Musolino, C., Ecker, C., & Rezzolla, L. 2024, ApJ, 962, 61, doi: 10.3847/1538-4357/ad1758

    work page doi:10.3847/1538-4357/ad1758 2024

  48. [48]

    R., & Rezzolla, L

    Nathanail, A., Most, E. R., & Rezzolla, L. 2021, ApJL, 908, L28, doi: 10.3847/2041-8213/abdfc6

    work page doi:10.3847/2041-8213/abdfc6 2021

  49. [49]

    2025, Inferring the neutron star equation of state with nuclear-physics informed semiparametric models, Class

    Ng, S., Legred, I., Suleiman, L., et al. 2025, Classical and Quantum Gravity, 42, 205008, doi: 10.1088/1361-6382/ae1094

    work page doi:10.1088/1361-6382/ae1094 2025

  50. [50]

    1998, A&AS, 132, 431, doi: 10.1051/aas:1998304

    Nozawa, T., Stergioulas, N., Gourgoulhon, E., & Eriguchi, Y. 1998, A&AS, 132, 431, doi: 10.1051/aas:1998304

    work page doi:10.1051/aas:1998304 1998

  51. [51]

    J., Tootle, S

    Papenfort, L. J., Tootle, S. D., Grandcl´ ement, P., Most, E. R., & Rezzolla, L. 2021, PhRvD, 104, 024057, doi: 10.1103/PhysRevD.104.024057

    work page doi:10.1103/physrevd.104.024057 2021

  52. [52]

    2010, Classical and Quantum Gravity, 27, 084007, 10.1088/0264-9381/27/8/084007

    Punturo, M., Abernathy, M., Acernese, F., et al. 2010, Classical and Quantum Gravity, 27, 084007, doi: 10.1088/0264-9381/27/8/084007

    work page doi:10.1088/0264-9381/27/8/084007 2010

  53. [53]

    2018, The Astrophysical Journal Letters, 852, L29, doi: 10.3847/2041-8213/aaa402

    Radice, D., Perego, A., Zappa, F., & Bernuzzi, S. 2018, ApJL, 852, L29, doi: 10.3847/2041-8213/aaa402

    work page doi:10.3847/2041-8213/aaa402 2018

  54. [54]

    A., ¨Ozel, F., & Psaltis, D

    Raithel, C. A., ¨Ozel, F., & Psaltis, D. 2018, ApJL, 857, L23, doi: 10.3847/2041-8213/aabcbf

    work page doi:10.3847/2041-8213/aabcbf 2018

  55. [55]

    T., Somasundaram, R., De, S., et al

    Reed, B. T., Somasundaram, R., De, S., et al. 2024, ApJ, 974, 285, doi: 10.3847/1538-4357/ad737c

    work page doi:10.3847/1538-4357/ad737c 2024

  56. [56]

    Cosmic Explorer: The U.S. Contribution to Gravitational-Wave Astronomy beyond LIGO

    Reitze, D., Adhikari, R. X., Ballmer, S., et al. 2019, in Bulletin of the American Astronomical Society, Vol. 51, 35, doi: 10.48550/arXiv.1907.04833

    work page internal anchor Pith review doi:10.48550/arxiv.1907.04833 2019

  57. [57]

    E., Watts, A

    Riley, T. E., Watts, A. L., Bogdanov, S., et al. 2019, Astrophys. J. Lett., 887, L21, doi: 10.3847/2041-8213/ab481c

    work page doi:10.3847/2041-8213/ab481c 2019

  58. [58]

    E., Watts, A

    Riley, T. E., Watts, A. L., Ray, P. S., et al. 2021, Astrophys. J. Lett., 918, L27, doi: 10.3847/2041-8213/ac0a81

    work page doi:10.3847/2041-8213/ac0a81 2021

  59. [59]

    and Kandel, D

    Romani, R. W., Kandel, D., Filippenko, A. V., Brink, T. G., & Zheng, W. 2022, ApJL, 934, L17, doi: 10.3847/2041-8213/ac8007

    work page doi:10.3847/2041-8213/ac8007 2022

  60. [60]

    2012, Classical and Quantum Gravity, 29, 124013, doi: 10.1088/0264-9381/29/12/124013

    Sathyaprakash, B., Abernathy, M., Acernese, F., et al. 2012, Classical and Quantum Gravity, 29, 124013, doi: 10.1088/0264-9381/29/12/124013

    work page doi:10.1088/0264-9381/29/12/124013 2012

  61. [61]

    Shawqi, S., Konstantinou, A., & Morsink, S. M. 2025, arXiv e-prints, arXiv:2508.18434, doi: 10.48550/arXiv.2508.18434

    work page internal anchor Pith review doi:10.48550/arxiv.2508.18434 2025

  62. [62]

    2022, JCAP, 2022, 071, doi: 10.1088/1475-7516/2022/08/071 —

    Soma, S., Wang, L., Shi, S., St ¨ocker, H., & Zhou, K. 2022, JCAP, 2022, 071, doi: 10.1088/1475-7516/2022/08/071 —. 2023, PhRvD, 107, 083028, doi: 10.1103/PhysRevD.107.083028

    work page doi:10.1088/1475-7516/2022/08/071 2022

  63. [63]

    , archivePrefix = "arXiv", eprint =

    Steiner, A. W., Hempel, M., & Fischer, T. 2013, ApJ, 774, 17, doi: 10.1088/0004-637X/774/1/17

    work page doi:10.1088/0004-637x/774/1/17 2013

  64. [64]

    Stergioulas, N., & Friedman, J. L. 1995, ApJ, 444, 306, doi: 10.1086/175605

    work page doi:10.1086/175605 1995

  65. [65]

    Suleiman, A

    Suleiman, L., Fantina, A. F., Gulminelli, F., & Read, J. 2025, arXiv e-prints, arXiv:2512.05315, doi: 10.48550/arXiv.2512.05315

    work page doi:10.48550/arxiv.2512.05315 2025

  66. [66]

    D., Jacques, T

    Tootle, S. D., Jacques, T. P., & Cassing, M. 2026, arXiv e-prints, arXiv:2601.05176, doi: 10.48550/arXiv.2601.05176

    work page doi:10.48550/arxiv.2601.05176 2026

  67. [67]

    Traversi, P

    Traversi, S., Char, P., & Pagliara, G. 2020, ApJ, 897, 165, doi: 10.3847/1538-4357/ab99c1

    work page doi:10.3847/1538-4357/ab99c1 2020

  68. [68]

    2025, PhRvD, 112, 043018, doi: 10.1103/bmb3-ktz6

    Tu, Z., Sun, X., Han, S., Miao, Z., & Li, A. 2025, PhRvD, 112, 043018, doi: 10.1103/bmb3-ktz6

    work page doi:10.1103/bmb3-ktz6 2025

  69. [69]

    Typel, S., R¨opke, G., Kl¨ahn, T., Blaschke, D., & Wolter, H. H. 2010, PhRvC, 81, 015803, doi: 10.1103/PhysRevC.81.015803

    work page doi:10.1103/physrevc.81.015803 2010

  70. [70]

    C., & Stuchl´ ık, Z

    Urbanec, M., Miller, J. C., & Stuchl´ ık, Z. 2013, MNRAS, 433, 1903, doi: 10.1093/mnras/stt858 van den Oord, A., Dieleman, S., Zen, H., et al. 2016, arXiv e-prints, arXiv:1609.03499, doi: 10.48550/arXiv.1609.03499

    work page doi:10.1093/mnras/stt858 2013

  71. [71]

    2024, arXiv e-prints, arXiv:2407.15753, doi: 10.48550/arXiv.2407.15753

    Vilkha, A., Yelikar, A., O’Shaughnessy, R., & Read, J. 2024, arXiv e-prints, arXiv:2407.15753, doi: 10.48550/arXiv.2407.15753

    work page doi:10.48550/arxiv.2407.15753 2024

  72. [72]

    Walker, K., Smith, R., Thrane, E., & Reardon, D. J. 2024, PhRvD, 110, 043013, doi: 10.1103/PhysRevD.110.043013

    work page doi:10.1103/physrevd.110.043013 2024

  73. [73]

    2020, ApJS, 250, 6, doi: 10.3847/1538-4365/aba2f3

    Wang, B., Zhu, Z., Li, A., & Zhao, W. 2020, ApJS, 250, 6, doi: 10.3847/1538-4365/aba2f3

    work page doi:10.3847/1538-4365/aba2f3 2020

  74. [74]

    2018, PhRvC, 98, 054618, doi: 10.1103/PhysRevC.98.054618

    Wang, R., Chen, L.-W., & Zhou, Y. 2018, PhRvC, 98, 054618, doi: 10.1103/PhysRevC.98.054618

    work page doi:10.1103/physrevc.98.054618 2018

  75. [75]

    2025, PhRvC, 111, 054605, doi: 10.1103/PhysRevC.111.054605 11

    Wang, S.-P., Li, X., Wang, R., Ye, J.-T., & Chen, L.-W. 2025, PhRvC, 111, 054605, doi: 10.1103/PhysRevC.111.054605 11

    work page doi:10.1103/physrevc.111.054605 2025

  76. [76]

    2024, PhRvC, 109, 054623, doi: 10.1103/PhysRevC.109.054623

    Wang, S.-P., Wang, R., Ye, J.-T., & Chen, L.-W. 2024, PhRvC, 109, 054623, doi: 10.1103/PhysRevC.109.054623

    work page doi:10.1103/physrevc.109.054623 2024

  77. [77]

    R., Most, E

    Weih, L. R., Most, E. R., & Rezzolla, L. 2019, ApJ, 881, 73, doi: 10.3847/1538-4357/ab2edd

    work page doi:10.3847/1538-4357/ab2edd 2019

  78. [78]

    Wouters, T., Pang, P. T. H., Koehn, H., et al. 2025a, arXiv e-prints, arXiv:2504.15893, doi: 10.48550/arXiv.2504.15893 —. 2025b, PhRvD, 112, 043037, doi: 10.1103/v2y8-kxvx

    work page doi:10.48550/arxiv.2504.15893

  79. [79]

    arXiv:2502.09200

    Wu, Z., Biswas, B., & Rosswog, S. 2025, arXiv e-prints, arXiv:2502.09200, doi: 10.48550/arXiv.2502.09200

    work page doi:10.48550/arxiv.2502.09200 2025

  80. [80]

    B., O’Shaughnessy, R., Wysocki, D., & Wade, L

    Yelikar, A. B., O’Shaughnessy, R., Wysocki, D., & Wade, L. 2024, arXiv e-prints, arXiv:2410.14674, doi: 10.48550/arXiv.2410.14674

    work page doi:10.48550/arxiv.2410.14674 2024

Showing first 80 references.