Pith. sign in

REVIEW 3 major objections 5 minor 6 cited by

CompactObject: An open-source Python package for full-scope neutron star equation of state inference

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read The paper presents CompactObject, an open-source Python package that runs a complete neutron star equation-of-state inference workflow, from EOS generation through TOV solving to Bayesian parameter estimation against astrophysical and…

desk verdict A genuinely useful open-source neutron-star EOS inference package, but the paper's core numerical validation is asserted rather than demonstrated; it needs a benchmark section before it can be called robust. read the letter →

arxiv 2411.14615 v1 pith:MER7KSH3 submitted 2024-11-14 astro-ph.HE astro-ph.IMnucl-th

Chun Huang , Tuhin Malik , João Cartaxo , Shashwat Sourav , Wenli Yuan , Tianzhe Zhou , Xuezhi Liu , John Groger
show 9 more authors
Xieyuan Dong Nicole Osborn Nathan Whitsett Zhiheng Wang Constança Providência Micaela Oertel Alexander Y. Chen Laura Tolos Anna Watts
This is my paper · ORCID
classification astro-ph.HEastro-ph.IMnucl-th
keywords neutronstarequationofstateBayesianinferenceTOVsolvernuclearastrophysicsgravitationalwavesX-raytimingquarkstarsopen-sourcesoftware
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper presents CompactObject, an open-source Python package that performs Bayesian inference on the neutron star equation of state (EOS). The central claim is that one modular workflow can turn X-ray timing, gravitational-wave, radio, and nuclear-physics data into posterior constraints on physically motivated EOS models, not just flexible meta-models. This matters because physics-driven models let researchers test concrete microphysical scenarios, such as hyperonic matter, quark stars, or strangeon stars, against the same datasets. The package's three independent components—EOS generation, TOV solving, and inference—can be reused separately, and it interfaces with the CompOSE database to add further EOS options.

What carries the argument

The load-bearing object is the three-module pipeline. The EOS generator produces pressure-density relations from seven model families; the Tolman-Oppenheimer-Volkoff (TOV) equations, the relativistic hydrostatic equilibrium equations linking pressure to mass and radius, are solved to predict observable quantities; and the inference module compares those predictions with data through dedicated likelihood routines and a nested-sampling backend for Bayesian evidence. Each module is independently callable, and the CompOSE interface extends the generator to existing EOS databases.

What would settle it

A concrete check: run CompactObject on a known analytic EOS, such as a polytrope or a constant-speed-of-sound model, and compare the computed mass-radius curve and tidal deformability with independent exact or published numerical solutions; a mismatch beyond solver tolerance, or a failure to reproduce a published posterior from the package's own earlier RMF analyses, would settle whether the pipeline is reliable.

Watch

Extended reading notes

Core claim

The central claim is that the full EOS inference loop can be assembled into a single auditable open-source package. CompactObject provides seven EOS choices (polytropic and speed-of-sound meta-models, relativistic mean field and density-dependent RMF models, MIT bag and strangeon quark-star models, and an NJL-based quark matter EOS), a TOV solver that computes mass-radius and tidal deformability, and a Bayesian inference workflow that applies likelihoods from X-ray timing, gravitational waves, radio masses, saturation properties, pQCD, and chiral EFT. The authors state that the package achieves high accuracy and rapid computation for physics-motivated EOSs and has already been used to constrain nucleonic and hyperonic RMF models, with quark-star and phase-transition applications in progress.

Load-bearing premise

The load-bearing premise is that every numerical routine in the package—the TOV integration, the X-ray, gravitational-wave, and radio likelihoods, and the pQCD/chiral EFT constraint implementations—is correct and mutually consistent, since the paper describes the architecture and prior applications but presents no independent validation or benchmark section.

Editorial extensions

If this is right

  • A researcher can run a complete EOS inference, from model choice to posterior constraints, inside one package with documented code.
  • Physics-motivated models (hyperonic RMF, quark stars, strangeon stars) can be compared against meta-models on equal footing using Bayesian evidence.
  • The TOV solver and EOS generator can be reused as standalone tools in nuclear-physics studies that do not need the full inference workflow.
  • Existing CompOSE EOS tables become directly testable against current astrophysical and nuclear constraints.
  • An MIT-licensed, archived release makes published EOS constraints reproducible by other groups.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The modular architecture invites component-level benchmarking: users could test the TOV solver against known analytic solutions to verify the package's accuracy independently of its own documentation.
  • If the pipeline is widely adopted, cross-comparison of Bayesian evidence across EOS models could become a routine, reproducible step in the field.
  • A natural stress test the authors do not report is an end-to-end reproduction of a published posterior (for instance, from their own RMF studies) using only the public package and data files.
  • The stated speed advantage rests on Numba-accelerated numerics; timing and convergence checks on large nested-sampling runs would make the performance claim concrete.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces CompactObject, an open-source Python package for Bayesian inference of the neutron star equation of state (EOS). The package consists of three modular components: an EOS generator supporting seven model classes (polytropic, speed-of-sound, RMF, density-dependent RMF, MIT bag, strangeon, and NJL quark matter), a Tolman-Oppenheimer-Volkoff (TOV) solver that computes mass-radius relations and tidal deformability, and a Bayesian inference workflow with UltraNest and emcee samplers. It integrates astrophysical constraints from X-ray timing, gravitational waves, and radio timing, as well as nuclear physics constraints from saturation properties, perturbative QCD, and chiral effective field theory, and can interface with the CompOSE EOS database. The paper describes the architecture, lists dependencies, cites prior and ongoing applications of the package by overlapping author groups, and points to the GitHub repository and Zenodo archive.

Significance. If the package is fully functional, it would provide a valuable community resource: a single auditable pipeline connecting physics-motivated EOS models to multi-messenger and nuclear Bayesian inference. The authors have released the code under the MIT license with a tagged GitHub repository and a Zenodo DOI, and the modular separation of EOS generation, TOV solution, and inference is a sensible design choice. The claimed integration of X-ray timing, GW tidal deformability, radio masses, pQCD, and chiral EFT constraints in one framework is significant. However, the paper contains no quantitative validation of the numerical or statistical core, so the strengths are currently potential rather than demonstrated. The package's correctness is what would make the platform 'robust', and that correctness is asserted rather than shown.

major comments (3)
  1. [Statement of need] The central claim that CompactObject 'achieves high accuracy and rapid computation' and provides a 'robust platform' is not supported by any quantitative validation in the manuscript. There is no benchmark section, no analytic test case for the TOV solver, and no comparison against independent TOV codes or published mass-radius/tidal-deformability results. Because the Bayesian inference pipeline is built on these numerical modules, a wrong or inconsistent implementation would produce misleading EOS posteriors while the package still runs. Please add a validation section demonstrating, at minimum: (i) convergence of the TOV integration against exact solutions (e.g., constant-density or polytropic stars) and against established solvers; (ii) recovery of injected parameters or known EOSs from simulated X-ray, GW, and radio data; and (iii) timing benchmarks that justify 'rapid computation'.
  2. [The CompactObject Package and scientific use] The evidence that the package components are correct currently rests on citations to prior and ongoing applications by largely overlapping author groups: Huang et al. (2024a,b), Yuan et al. (2024), and Malik et al. (2022,2023). These are self-citations rather than independent validation. The manuscript should either describe the specific validation performed in those works for the particular modules released here and explain why that validation transfers to version v1.9, or provide direct cross-checks, such as reproducing a known EOS's mass-radius relation and tidal deformability from CompOSE tables, or comparing the X-ray timing likelihood implementation against independently published analyses. Without such information, the 'robust platform' claim is not verifiable from the paper.
  3. [The CompactObject Package and scientific use] The inference workflow is described as a 'complete pipeline' with UltraNest and emcee options, but the paper gives no information on convergence tests, number of live points, evidence stability, or validation on synthetic data. Since the package is intended for Bayesian model comparison via nested-sampling evidence, the absence of any test of the evidence computation is a load-bearing gap. Please add at least one end-to-end test where a known EOS model is used to simulate observables, the pipeline recovers the injected parameters, and the recovered evidence matches an analytic or independent calculation.
minor comments (5)
  1. [Statement of need] The sentence 'The package's user-friendly interface and modular architecture facilitates a easy adoption' contains a grammatical error ('a easy' should be 'an easy') and a subject-verb agreement issue; please revise.
  2. [References] The GitHub reference (Huang et al. 2023) spells 'Malick' instead of 'Malik' and the repository title contains 'nference' instead of 'inference'. Please correct these typos in the reference list.
  3. [References] The citation 'Raaijmakers, G., Rutherford, N., Timmerman, P., et al. 2023, JOSS submitted' is not a stable archival reference; please cite the published version or provide an arXiv identifier.
  4. [The CompactObject Package and scientific use] Yuan et al. (2024) is cited as 'to be submitted'; for a published software paper, this ongoing work should be labeled as in preparation or removed, and its role should not be used as evidence of the package's correctness.
  5. [Summary] The Summary is nearly a verbatim copy of the Abstract. Consider shortening the Summary to focus on the distinguishing features of the software rather than repeating the abstract verbatim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found: the package's central claims are asserted performance and prior use, not a derivation that reduces to its own inputs.

full rationale

This is a software description paper rather than a derivation chain. The central claims are that CompactObject 'achieves high accuracy and rapid computation' and provides an 'open-source, robust platform designed for Bayesian inference on neutron star EOS constraints.' Nothing in the text defines an output quantity in terms of an input so that a prediction is equal to its premise by construction. The self-citations to Huang et al. 2024a,b, Malik et al. 2022,2023, and Yuan et al. 2024 are presented as evidence that the package has been used ('CompactObject has been utilized to derive constraints on nucleonic RMF models'), not as a proof that its numerical core is accurate. A prior use by the same authors is not independent validation, but the manuscript does not invoke those papers as a theorem or as a derivation of the accuracy claim; the accuracy claim is simply unvalidated in the visible text. That is a validation gap and a correctness risk, but not circularity: the claim could be false without being tautological. The code is released on GitHub and Zenodo, making external benchmarking possible, which further distinguishes an unverified claim from a self-referential one. No equation-level reduction, no fitted parameter renamed as a prediction, and no uniqueness theorem imported from the authors appears. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper contributes software, not a new derivation, so there are no fitted constants or invented physical entities in the visible text. The central claim instead assumes standard TOV physics, standard Bayesian sampling, and the validity of the imported nuclear and astrophysical constraints.

assumptions (4)
  • domain assumption The Tolman-Oppenheimer-Volkoff equations describe the equilibrium structure of neutron stars.
    The package's mass-radius and tidal deformability outputs are generated by solving TOV; this standard physics is assumed without derivation in the package description.
  • domain assumption X-ray timing, gravitational wave, radio, pQCD, and chiral EFT constraints can be combined as approximately independent likelihoods in a joint Bayesian analysis.
    The 'full-scope' inference workflow integrates these datasets; the paper does not discuss correlations or consistency checks between them.
  • domain assumption The implemented EOS models (RMF, density-dependent RMF, NJL, MIT bag, strangeon, polytropic, speed-of-sound) faithfully represent the physics they are claimed to encode.
    The package's usefulness depends on correct transcription of these models from the cited literature; the visible text does not include verification against published EOS tables.
  • standard math UltraNest and emcee produce converged posterior estimates and evidence values for the implemented likelihoods.
    The package relies on these third-party samplers; convergence is a tooling assumption not demonstrated in the visible text.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CompactObject: An open-source Python package for full-scope neutron star equation of state inference." pith.science (2026). https://pith.science/paper/MER7KSH3

@misc{pith2026241114615,
  author       = {Pith},
  title        = {Pith review of: CompactObject: An open-source Python package for full-scope neutron star equation of state inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MER7KSH3}},
  note         = {Machine review of arXiv:2411.14615}
}
abstract

The CompactObject package is an open-source software framework developed to constrain the neutron star equation of state (EOS) through Bayesian statistical inference. It integrates astrophysical observational constraints from X-ray timing, gravitational wave events, and radio measurements, as well as nuclear experimental constraints derived from perturbative Quantum Chromodynamics (pQCD) and Chiral Effective Field Theory ($\chi$EFT). The package supports a diverse range of EOS models, including meta-model like and several physics-motivated EOS models. It comprises three independent components: an EOS generator module that currently provides seven EOS choices, a Tolman-Oppenheimer-Volkoff (TOV) equation solver, that allows the determination of the Mass Radius and Tidal deformability as observables, and a comprehensive Bayesian inference workflow module, including a complete pipeline for implementing EOS Bayesian inference. Each component can be used independently in different scientific research contexts, such as nuclear physics and astrophysics. In addition, CompactObject is designed to work in synergy with existing software such as CompOSE, allowing the use of the CompOSE EOS database to extend the EOS options available.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

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

  1. Neutron stars can shine a light on elusive lepton-flavor-violating dark matter

    hep-ph 2025-11 conditional novelty 7.0 of 10

    Flavor blocking keeps lepton-flavor-violating dark matter from thermalizing inside neutron stars, so p-wave annihilation stays efficient and heats the star to observable temperatures.

  2. Dark Matter Heating of Compact Stars Beyond Capture: A Relativistic Framework for Energy Deposition by Particle Beams

    hep-ph 2026-02 conditional novelty 6.0 of 10

    A new relativistic formalism computes capture and energy deposition of directed particle beams in compact stars, applied to blazar-boosted dark matter heating of white dwarfs and neutron stars.

  3. Equation of State Independent Determination on the Radius of a 1.4 $M_{\odot}$ Neutron Star Using Mass-Radius Measurements

    astro-ph.HE 2024-12 conditional novelty 5.0 of 10

    R1.4 is estimated directly from NICER posteriors of PSR J0030+0451 and PSR J0437-4715: about 11.7 km under a two-source interpolation scenario and 12.1 to 12.7 km under a fixed 1.4-solar-mass scenario, depending on J0...

  4. Bayesian inference of strangeon matter using the measurements of PSR J0437-4715 and GW190814

    astro-ph.HE 2024-11 reject novelty 5.0 of 10

    A Bayesian fit of the strangeon-matter equation of state to NICER and LIGO/Virgo data prefers 18-quark clusters and maximum masses near 3.6 solar masses, but the 18-quark preference is an artifact of the chosen prior range.

  5. The equation of state for neutron stars with speed of sound constraints via Bayesian inference

    nucl-th 2025-09 conditional novelty 4.0 of 10

    A Taylor-expanded nuclear EOS is fit to NICER radii, crust-core transition constraints, and the causality condition, giving Q_sat = -69.5^{+16.5}_{-31.9} MeV and L_sym = 34.3^{+13.7}_{-11.9} MeV.

  6. Model-Independent Determination of the Tidal Deformability of a 1.4 $M_{\odot}$ Neutron Star from Gravitational-Wave Measurements

    astro-ph.HE 2025-05 conditional novelty 4.0 of 10

    Interpolating GW170817 mass and tidal deformability posteriors yields an equation of state agnostic tidal deformability for a 1.4 solar mass neutron star, Lambda_1.4 = 222.89 (+420.33, -98.85).

Reference graph

Works this paper leans on

35 extracted references · 8 canonical work pages · cited by 6 Pith papers

  1. [1]

    Bodmer, A. R. 1971, Physical Review D, 4, 1601, doi: 10.1103/PhysRevD.4.1601

    doi:10.1103/physrevd.4.1601 1971

  2. [2]

    2005, Physica Reports, 407, 205, doi: 10.1016/j.physrep.2004.11.004

    Buballa, M. 2005, Physica Reports, 407, 205, doi: 10.1016/j.physrep.2004.11.004

    doi:10.1016/j.physrep.2004.11.004 2005

  3. [3]

    2021, The Journal of Open Source Software, 6, 300 1, doi: 10.21105/joss.03001

    Buchner, J. 2021, The Journal of Open Source Software, 6, 300 1, doi: 10.21105/joss.03001

    doi:10.21105/joss.03001 2021

  4. [4]

    2014, Physical Review C, 90, doi: 10.1103/physrevc.90.015801

    Char, P., & Banik, S. 2014, Physical Review C, 90, doi: 10.1103/physrevc.90.015801

    work page doi:10.1103/physrevc.90.015801 2014

  5. [5]

    2023, Phys

    Char, P., Mondal, C., Gulminelli, F., & Oertel, M. 2023, Phys . Rev. D, 108, 103045, doi: 10.1103/PhysRevD.108.103045

    work page doi:10.1103/physrevd.108.103045 2023

  6. [6]

    L., Johnson, K., Thorn, C

    Chodos, A., Jaffe, R. L., Johnson, K., Thorn, C. B., & Weisskop f, V. F. 1974, Phys. Rev. D, 9, 3471, doi: 10.1103/PhysRevD.9.3471 Dalcín, L., Paz, R., Storti, M., & D’Elía, J. 2008, Journal of Parallel and Distributed Computing, 68, 655, doi: https://doi.org/10.1016/j.jpdc.2007.09.005

    doi:10.1103/physrevd.9.3471 1974

  7. [7]

    2016, The Journal of Open Source Softwar e, 1, 24, doi: 10.21105/joss.00024

    Foreman-Mackey, D. 2016, The Journal of Open Source Softwar e, 1, 24, doi: 10.21105/joss.00024

    doi:10.21105/joss.00024 2016

  8. [8]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013 , Publications of the Astronomical Society of the Pacific, 125, 306–312, doi: 10.1086/670067

    doi:10.1086/670067 2013

Show all 35 references
  1. [9]

    2023, Astrophys

    Gorda, T., Komoltsev, O., & Kurkela, A. 2023, Astrophys. J., 950, 107, doi: 10.3847/1538-4357/acce3a

    2023 doi

  2. [10]

    M., Pethick, C

    Hebeler, K., Lattimer, J. M., Pethick, C. J., & Schwenk, A. 20 13, Astrophys. J., 773, 11, doi: 10.1088/0004-637X/773/1/11

    doi

  3. [11]

    2010, Nuclear Physics A, 837, 210–254, doi: 10.1016/j.nuclphysa.2010.02.010 4

    Hempel, M., & Schaffner-Bielich, J. 2010, Nuclear Physics A, 837, 210–254, doi: 10.1016/j.nuclphysa.2010.02.010 4

    2010 doi

  4. [12]

    2018, Physical Review Letter s, 120, 222001, doi: 10.1103/PhysRevLett.120.222001

    Holdom, B., Ren, J., & Zhang, C. 2018, Physical Review Letter s, 120, 222001, doi: 10.1103/PhysRevLett.120.222001

    2018 doi

  5. [13]

    L., Tolos, L., & Provid ência, C

    Huang, C., Raaijmakers, G., Watts, A. L., Tolos, L., & Provid ência, C. 2024a, Mon. Not. Roy. Astron. Soc., 529, 4650, doi: 10.1093/mnras/stae844

    doi

  6. [14]

    Huang, C., Tolos, L., Providência, C., & Watts, A. 2024b. https://arxiv.org/abs/2410.14572

    arXiv

  7. [15]

    2023, CompactObje ct: An open-source package for neutron star whole workflow Bayesian nference constraining Neutron star EOS package, Github

    Huang, C., Malick, T., Cartaxo, J., et al. 2023, CompactObje ct: An open-source package for neutron star whole workflow Bayesian nference constraining Neutron star EOS package, Github. https://github.com/ChunHuangPhy/EoS_inference/tree/v.1.9 —. 2024c, ChunHuangPhy/EoS_inference...

    2023 doi

  8. [16]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90 , doi: 10.1109/MCSE.2007.55

    2007 doi

  9. [17]

    2022, Nature, 606, 276, doi: 10.1038/s41586-022-04750-w

    Huth, S., et al. 2022, Nature, 606, 276, doi: 10.1038/s41586-022-04750-w

    2022 doi

  10. [18]

    Klevansky, S. P. 1992, Reviews of Modern Physics, 64, 649, do i: 10.1103/RevModPhys.64.649

    1992 doi

  11. [19]

    2016, in IOS Press, 87–90, doi: 10.3233/978-1-61499-649-1-87

    Kluyver, T., Ragan-Kelley, B., Pérez, F., et al. 2016, in IOS Press, 87–90, doi: 10.3233/978-1-61499-649-1-87

    2016 doi

  12. [20]

    K., Pitrou, A., & Seibert, S

    Lam, S. K., Pitrou, A., & Seibert, S. 2015, in Proceedings of t he Second Workshop on the LL VM Compiler Infrastructure in HPC, LL VM ’15 (New York, NY, USA: Association for Computing Machinery), doi: 10.1145/2833157.2833162

    2015

  13. [21]

    K., & Providência, C

    Malik, T., Ferreira, M., Agrawal, B. K., & Providência, C. 20 22, Astrophys. J., 930, 17, doi: 10.3847/1538-4357/ac5d3c

    doi

  14. [22]

    B., & Providência, C

    Malik, T., Ferreira, M., Albino, M. B., & Providência, C. 202 3, Phys. Rev. D, 107, 103018, doi: 10.1103/PhysRevD.107.103018

    doi

  15. [23]

    Oliphant, T. E. 2007, Computing in Science and Engg., 9, 10–2 0, doi: 10.1109/MCSE.2007.58 Providência, C., Malik, T., Albino, M. B., & Ferreira, M. 202 3, doi: 10.1201/9781003306580-5

    2007 doi

  16. [24]

    2023 , JOSS submitted

    Raaijmakers, G., Rutherford, N., Timmerman, P., et al. 2023 , JOSS submitted

    2023

  17. [25]

    G., & Piekarewicz, J

    Todd-Rutel, B. G., & Piekarewicz, J. 2005, Phys. Rev. Lett., 95, 122501, doi: 10.1103/PhysRevLett.95.122501

    2005 doi

  18. [26]

    2016, The Astrophysica l Journal, 834, 3, doi: 10.3847/1538-4357/834/1/3 —

    Tolos, L., Centelles, M., & Ramos, A. 2016, The Astrophysica l Journal, 834, 3, doi: 10.3847/1538-4357/834/1/3 —. 2017, Publications of the Astronomical Society of Austra lia, 34, e065, doi: 10.1017/pasa.2017.60

    2016 doi

  19. [27]

    2015, Phys

    Typel, S., Oertel, M., & Klähn, T. 2015, Phys. Part. Nucl., 46 , 633, doi: 10.1134/S1063779615040061

    2015 doi

  20. [28]

    Typel, S., & Wolter, H. H. 1999, Nucl. Phys. A, 656, 331, doi: 10.1016/S0375-9474(99)00310-3

    1999 doi

  21. [29]

    2022, Eur

    Typel, S., et al. 2022, Eur. Phys. J. A, 58, 221, doi: 10.1140/epja/s10050-022-00847-y 5 van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Comput ing in Science & Engineering, 13, 22–30, doi: 10.1109/mcse.2011.37

    2022 doi

  22. [30]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Natu re Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  23. [31]

    Waskom, M. L. 2021, Journal of Open Source Software, 6, 3021, doi: 10.21105/joss.03021

    2021 doi

  24. [32]

    1984, Physical Review D, 30, 272, doi: 10.1103/PhysRevD.30.272

    Witten, E. 1984, Physical Review D, 30, 272, doi: 10.1103/PhysRevD.30.272

    1984 doi

  25. [33]

    2023, NumbaMinpack, https://github.com/Nicholaswogan/NumbaMinpack

    Wogan, N. 2023, NumbaMinpack, https://github.com/Nicholaswogan/NumbaMinpack

    2023

  26. [34]

    Xu, R. X. 2003, ApJL, 596, L59, doi: 10.1086/379209

    2003 doi

  27. [35]

    2024, to be su bmitted 6

    Yuan, W., Huang, C., Zhang, C., Zhou, E., & Xu, R. 2024, to be su bmitted 6

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.