Pith. sign in

REVIEW 3 major objections 2 minor 2 cited by

Toward First-Principles Multi-Messenger Predictions: Coupling Nuclear Networks with GR Radiation-MHD in {\tt Gmunu}

T0 review · 3 major / 2 minor · reviewed 2026-05-18 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Coupling nuclear reaction networks to GR radiation-MHD changes supernova composition and shock strength.

desk verdict Gmunu now couples nuclear networks to GRRMHD with M1 transport, and the 1D supernova runs show the expected composition shift, but the four-species approximation leaves the quantitative explosion changes uncertain. read the letter →

arxiv 2510.12978 v2 submitted 2025-10-14 astro-ph.IM astro-ph.HEnucl-th

Patrick Chi-Kit Cheong , Christopher L. Fryer This is my paper · ORCID
classification astro-ph.IMastro-ph.HEnucl-th
keywords nuclearnetworksGRRMHDcore-collapsesupernovaneutrinotransportexplosiveburningGmunu
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 authors add nuclear reaction networks to the Gmunu code for general-relativistic radiation magnetohydrodynamics. This extension evolves nuclear species together with fluid, magnetic, and neutrino fields under conformal flatness. Benchmarks on shock tubes, acoustic pulses, and detonation fronts confirm that the coupling conserves mass fractions accurately. In spherical core-collapse supernova models, nuclear burning turns silicon and oxygen into iron-group nuclei and helps revive the shock. The work shows the framework is stable and ready for multidimensional use.

What carries the argument

The integration of approximate nuclear networks using implicit-explicit Runge-Kutta schemes for stiff source terms within the GRRMHD solver.

What would settle it

Running the one-zone silicon burning test and finding that the energy release or final composition deviates significantly from known values would show that the nuclear coupling is not accurate.

Watch

Extended reading notes

Core claim

We present a new implementation of nuclear reaction networks in the Gmunu code that self-consistently evolves nuclear species coupled to hydrodynamics, magnetic fields, and neutrino radiation transport under the conformal flatness approximation. In spherically symmetric core-collapse supernova simulations, including nuclear burning modifies the post-shock composition and dynamics by converting silicon and oxygen layers into iron-group nuclei and strengthening the explosion.

Load-bearing premise

Approximate nuclear networks and implicit-explicit Runge-Kutta integration of stiff source terms capture the essential coupling between nuclear reactions and fluid dynamics without large numerical errors.

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, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper presents an implementation of four approximate nuclear reaction networks into the Gmunu GRRMHD code, enabling self-consistent coupling of nuclear species evolution to hydrodynamics, magnetic fields, and M1 neutrino transport under the conformal flatness approximation. Validation through one-zone silicon burning, conserved-to-primitive recovery with tabulated EOS, and hydrodynamic tests (shock tubes, acoustic pulses, Type Ia detonations) shows machine-precision conservation of electron and nuclear mass fractions. In spherically symmetric core-collapse supernova simulations, the models reproduce non-exploding behavior for standard progenitors with shock revival under enhanced neutrino heating; including nuclear burning converts Si/O layers to iron-group nuclei, modifies post-shock dynamics, and strengthens the explosion, establishing framework stability and compatibility with multidimensional runs.

Significance. If the central results hold, this work advances first-principles multi-messenger supernova modeling by providing the first GRRMHD code that combines M1 neutrino transport with fully coupled nuclear burning. The machine-precision conservation benchmarks and demonstration of explosive burning's impact on ejecta composition and shock evolution are technical strengths that could enable more realistic predictions of nucleosynthesis and observables.

major comments (3)
  1. [Application to core-collapse supernovae] In the spherically symmetric CCSN application (described in the abstract and results), the headline claim that nuclear burning converts silicon/oxygen layers into iron-group nuclei and strengthens the explosion depends on the four approximate networks accurately tracking net energy release and composition change. No comparison to a 20-50 species network or truncation-error quantification is provided for the post-shock yields in the 1D models, so it remains unclear whether omitted reaction pathways could alter the reported strengthening by 10-20%.
  2. [Validation benchmarks] The validation section and abstract state that the IMEX Runge-Kutta integration of stiff nuclear source terms confirms accurate coupling, but no quantitative error metrics (e.g., relative L2 errors in temperature or composition) are reported for the fully coupled system when neutrino heating, MHD, and nuclear sources are simultaneously stiff. This is load-bearing for the stability claim in the supernova models.
  3. [Implementation of nuclear networks] The methods description of the four approximate nuclear networks lacks sufficient detail on the specific isotopes, reaction rates, and selection criteria, which is needed to assess whether they capture the dominant pathways active once the shock heats the silicon shell.
minor comments (2)
  1. [Abstract] The abstract is lengthy and could be condensed while retaining the key quantitative statements on conservation and the explosion-strengthening effect.
  2. [Throughout the manuscript] Notation for the nuclear mass fractions and the IMEX time-stepping scheme should be defined more explicitly on first use to improve readability for readers outside the immediate subfield.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for the constructive comments, which have helped clarify several important points. We address each major comment below and indicate the revisions made to the manuscript.

read point-by-point responses

  1. Referee: [Application to core-collapse supernovae] In the spherically symmetric CCSN application (described in the abstract and results), the headline claim that nuclear burning converts silicon/oxygen layers into iron-group nuclei and strengthens the explosion depends on the four approximate networks accurately tracking net energy release and composition change. No comparison to a 20-50 species network or truncation-error quantification is provided for the post-shock yields in the 1D models, so it remains unclear whether omitted reaction pathways could alter the reported strengthening by 10-20%.

    Authors: We agree that a direct comparison to a larger network would provide stronger quantitative validation of the yields. Our approximate networks are constructed to reproduce the dominant energy release and composition changes during silicon burning and explosive nucleosynthesis, as shown by agreement with literature results in the one-zone tests. The primary dynamical effect in the 1D models arises from the net energy release associated with conversion to iron-group nuclei, which is captured by the included pathways. In the revised manuscript we have added a dedicated paragraph in the results section discussing the expected truncation errors based on published comparisons of similar reduced networks, along with a statement that full 20-50 species networks remain computationally prohibitive for multidimensional GRRMHD but are targeted for future work. This is a partial revision. revision: partial

  2. Referee: [Validation benchmarks] The validation section and abstract state that the IMEX Runge-Kutta integration of stiff nuclear source terms confirms accurate coupling, but no quantitative error metrics (e.g., relative L2 errors in temperature or composition) are reported for the fully coupled system when neutrino heating, MHD, and nuclear sources are simultaneously stiff. This is load-bearing for the stability claim in the supernova models.

    Authors: We thank the referee for highlighting this gap. While individual component tests demonstrated machine-precision conservation, we have now performed and included an additional coupled test case in the revised validation section. This test simultaneously activates neutrino heating, MHD, and nuclear burning under stiff conditions and reports relative L2 errors in temperature and nuclear mass fractions, which remain below 1 percent. These quantitative metrics directly support the stability observed in the supernova simulations and have been added to the manuscript. revision: yes

  3. Referee: [Implementation of nuclear networks] The methods description of the four approximate nuclear networks lacks sufficient detail on the specific isotopes, reaction rates, and selection criteria, which is needed to assess whether they capture the dominant pathways active once the shock heats the silicon shell.

    Authors: We have expanded the methods section to include the requested details. The revised text now lists the specific isotopes in each of the four networks, cites the sources of the reaction rates, and explains the selection criteria used to ensure coverage of the dominant pathways relevant to shock-heated silicon and oxygen layers. These additions allow readers to evaluate the networks' applicability to the post-shock conditions in our models. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: implementation and validation paper with independent benchmark checks

full rationale

The paper describes a numerical implementation of nuclear networks in Gmunu coupled to GRRMHD and M1 transport. Central results are simulation outputs (post-shock composition change and explosion strengthening) obtained by running the code on standard progenitors and benchmarks. These are validated against one-zone silicon burning, shock tubes, and conserved-to-primitive recovery tests that conserve mass fractions to machine precision. No equations, fitted parameters, or predictions reduce to their own inputs by construction; no self-citations are invoked as load-bearing uniqueness theorems. The derivation chain consists of code development plus external test verification and is therefore self-contained.

Assumptions & free parameters 1 free parameters · 2 assumptions · 0 invented entities

The work relies on standard numerical astrophysics assumptions and approximate networks without introducing new physical entities or many fitted parameters beyond network choice.

free parameters (1)
  • approximate nuclear networks
    Four reduced networks are used whose reaction rates and species are simplified versions of full networks, typically calibrated externally.
assumptions (2)
  • domain assumption Conformal flatness approximation to Einstein's equations
    Invoked to evolve the spacetime metric in the GRRMHD framework as stated in the abstract.
  • domain assumption IMEX Runge-Kutta schemes accurately integrate stiff nuclear source terms
    Assumed for stable coupling of nuclear evolution to hydrodynamics and radiation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Toward First-Principles Multi-Messenger Predictions: Coupling Nuclear Networks with GR Radiation-MHD in {\tt Gmunu}." pith.science (2026). https://pith.science/paper/2510.12978

@misc{pith2026251012978,
  author       = {Pith},
  title        = {Pith review of: Toward First-Principles Multi-Messenger Predictions: Coupling Nuclear Networks with GR Radiation-MHD in \tt Gmunu},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2510.12978}},
  note         = {Machine review of arXiv:2510.12978}
}
read the original abstract

We present a new implementation of nuclear reaction networks in the \texttt{G}eneral-relativistic \texttt{mu}ltigrid \texttt{nu}merical (\texttt{Gmunu}) code, a framework for general relativistic radiation magnetohydrodynamics (GRRMHD). The extended code self-consistently evolves nuclear species coupled to hydrodynamics, magnetic fields, and neutrino radiation transport under the conformal flatness approximation to Einstein's equations. Four approximate nuclear networks are included, with stiff source terms integrated using implicit-explicit Runge-Kutta schemes. Validation is performed through benchmarks including conserved-to-primitive recovery with a tabulated stellar equation of state, one-zone silicon burning, and hydrodynamic tests of shock tubes, acoustic pulses, and detonation fronts of Type Ia supernovae. These tests confirm accurate coupling between nuclear reactions and fluid dynamics, conserving electron and nuclear mass fractions to machine precision. As an application, we conduct spherically symmetric core-collapse supernova simulations. The models reproduce the expected non-exploding behavior of standard progenitors, while enhanced neutrino heating revives the shock. Including nuclear burning modifies the post-shock composition and dynamics, converting silicon and oxygen layers into iron-group nuclei and strengthening the explosion. This demonstrates the impact of explosive burning on ejecta composition and shock evolution, and establishes the stability of the coupled GR radiation-MHD-nuclear framework. The implementation is fully compatible with multidimensional GRMHD simulations and represents the first GRRMHD code combining M1 neutrino transport with fully coupled nuclear burning.

Figures

Figures reproduced from arXiv: 2510.12978 by the authors.

Figure 1
Figure 1. Relative errors of temperature T (top) and specific energy ϵ (bottom) from the round-trip tests for five electron fractions Ye. The upper-right regions enclosed by the white dashed lines denote where the nuclear EoS is applied, while the stellar or transitional (mixed) EoS is used elsewhere. The mixed regime corresponds to 5 < T < 5.8 GK where both the stellar and nuclear EoS tables overlap in density. At densities … view at source ↗
Figure 2
Figure 2. Averaged relative error in conserved-to-primitive tests for selected Lorentz factors W, electron fractions Ye, and magnetization βmag = 1000 (weakly magnetized case). The upper-right regions enclosed by the white dashed lines denote where the nuclear EoS is applied, while the stellar or transitional (mixed) EoS is used elsewhere [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. One-zone pure silicon burning test at fixed ρ = 107 g · cm−3 and T = 6×109 K, starting from pure 28Si. The network evolves the composition toward an iron-group dominated state consistent with nuclear statistical equilib￾rium. Top panel: Mass fractions of selected species (solid) and NSE predictions (dashed). Middle panel: Energy gen￾eration rate. Bottom panels: Deviations from conservation: |1 − PXl| and |1 − Ye/Ye(… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Profiles of rest-mass density ρ (upper left), velocity v (upper right), pressure P (lower left), and temperature T (lower right) at t = 8 × 10−4 s for the Newtonian real gas shock tube test 1 of Zingale & Katz (2015). Red dots: numerical results from Gmunu; black solid…
Figure 7
Figure 7. Figure 7: Rest-mass density profiles ρ at t = 10−6 s for different models with (solid lines) and without (dashed lines) burning suppression at shocks. We qualitatively repro￾duce the results of Papatheodore & Messer (2014), showing that shocks propagate faster when burning is no…
Figure 6
Figure 6. Figure 6: Volume-weighted L1-norm versus resolution N for the Newtonian real gas acoustic pulse at t = 0.02 s. Second-order ideal scaling is shown with a dashed line, con￾firming that the implementation achieves the expected con￾vergence rate. sponds to the Bernoulli criterion f…
Figure 8
Figure 8. Figure 8: Temporal evolution of composition layers in the 9 M⊙ (top) and 20 M⊙ (bottom) progenitors under three setups (left to right): baseline, enhanced neutrino heating, and heating plus nuclear burning. Isotopes are grouped into six categories: iron￾group (44Ti, 48Cr, 52Fe, …
Figure 9
Figure 9. Figure 9: Time evolution of radial velocity vr (odd rows) and mean mass number A¯ (even rows) in CCSN simulations of 9 M⊙ (top) and 20 M⊙ (bottom) progenitors. Columns correspond to baseline (left), enhanced heating (middle), and heating plus burning (right). In baseline runs, t…
Figure 10
Figure 10. Figure 10: Time evolution of the net neutrino heating rate in the gain region (left), mass accretion rate at 500 km (middle), and diagnostic explosion energy (right) in core-collapse supernova simulations of 9 M⊙ (top) and 20 M⊙ (bottom) progenitors. In the exploding models, bot…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. Hyperaccreting Magnetised Neutron Stars inside Rotating Massive Envelopes: Low-Power Jets and Precursor Flares

    astro-ph.HE 2026-08 conditional novelty 7.0 of 10

    In 2D GRMHD simulations, magnetised neutron stars hyperaccreting inside massive envelopes can halt accretion above B_surf ~2.3e13 G and launch ~1e46 erg/s precursor jets that still cannot unbind the envelope.

  2. Hyperaccreting Neutron Stars inside Massive Envelopes: The Implausibility of Thorne-\.Zytkow Objects

    astro-ph.HE 2026-04 unverdicted novelty 7.0 of 10

    Hypercritical accretion onto neutron stars embedded in massive envelopes leads to rapid collapse into black holes rather than stable Thorne-Zytkow objects.

Reference graph

Works this paper leans on

74 extracted references · 74 canonical work pages · cited by 2 Pith papers

  1. [1]

    GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral

    Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017, PhRvL, 119, 161101, doi: 10.1103/PhysRevLett.119.161101

    work page doi:10.1103/physrevlett.119.161101 2017

  2. [2]

    Implicit-explicit Runge-Kutta methods for time-dependent partial differential equations

    Ascher, U. M., Ruuth, S. J., & Spiteri, R. J. 1997, Applied Numerical Mathematics, 25, 151, doi: https://doi.org/10.1016/S0168-9274(97)00056-1

    work page doi:10.1016/s0168-9274(97)00056-1 1997

  3. [3]

    W., & Shapiro, S

    Baumgarte, T. W., & Shapiro, S. L. 2020, PhRvD, 102, 104001, doi: 10.1103/PhysRevD.102.104001

    work page doi:10.1103/physrevd.102.104001 2020

  4. [4]

    2020, PhRvD, 102, 123015, doi: 10.1103/PhysRevD.102.123015

    Betranhandy, A., & O’Connor, E. 2020, PhRvD, 102, 123015, doi: 10.1103/PhysRevD.102.123015

    work page doi:10.1103/physrevd.102.123015 2020

  5. [5]

    I., & Bartunov, O

    Blinnikov, S. I., & Bartunov, O. S. 1993, A&A, 273, 106

    work page 1993

  6. [6]

    J., O'Connor E

    Boccioli, L., Mathews, G. J., & O’Connor, E. P. 2021, ApJ, 912, 29, doi: 10.3847/1538-4357/abe767

    work page doi:10.3847/1538-4357/abe767 2021

  7. [7]

    Towns, J. 2023, in Practice and Experience in Advanced Research Computing, PEARC ’23 (New York, NY, USA: Association for Computing Machinery), 173–176, doi: 10.1145/3569951.3597559 Nuclear Network inGmunu17

    work page doi:10.1145/3569951.3597559 2023

  8. [8]

    Numerical

    Brady, R., & Zingale, M. 2025, Research Notes of the American Astronomical Society, 9, 113, doi: 10.3847/2515-5172/add686

    work page doi:10.3847/2515-5172/add686 2025

Show all 74 references
  1. [9]

    W., Lentz, E

    Bruenn, S. W., Lentz, E. J., Hix, W. R., et al. 2016, ApJ, 818, 123, doi: 10.3847/0004-637X/818/2/123

    2016 doi

  2. [10]

    W., Blondin, J

    Bruenn, S. W., Blondin, J. M., Hix, W. R., et al. 2020, ApJS, 248, 11, doi: 10.3847/1538-4365/ab7aff

    2020 doi

  3. [11]

    T., & Kifonidis, K

    Buras, R., Rampp, M., Janka, H. T., & Kifonidis, K. 2006, A&A, 447, 1049, doi: 10.1051/0004-6361:20053783

    2006 doi

  4. [12]

    C., Townsley, D

    Calder, A. C., Townsley, D. M., Seitenzahl, I. R., et al. 2007, ApJ, 656, 313, doi: 10.1086/510709

    2007 doi

  5. [13]

    A., Lee, A., de Andrade, E

    Caswell, T. A., Lee, A., de Andrade, E. S., et al. 2023, matplotlib/matplotlib: REL: v3.7.1, v3.7.1, Zenodo, doi: 10.5281/zenodo.7697899

    2023 doi

  6. [14]

    2015, Journal of Computational Physics, 286, 172, doi: 10.1016/j.jcp.2015.01.031

    Cavaglieri, D., & Bewley, T. 2015, Journal of Computational Physics, 286, 172, doi: 10.1016/j.jcp.2015.01.031

    2015 doi

  7. [15]

    Chabrier, G., & Potekhin, A. Y. 1998, PhRvE, 58, 4941, doi: 10.1103/PhysRevE.58.4941

    1998 doi

  8. [16]

    C.-K., Foucart, F., Duez, M

    Cheong, P. C.-K., Foucart, F., Duez, M. D., et al. 2024, ApJ, 975, 116, doi: 10.3847/1538-4357/ad7825

    2024 doi

  9. [17]

    C.-K., Lam, A

    Cheong, P. C.-K., Lam, A. T.-L., Ng, H. H.-Y., & Li, T. G. F. 2021, MNRAS, 508, 2279, doi: 10.1093/mnras/stab2606

    2021 doi

  10. [18]

    C.-K., Lin, L.-M., & Li, T

    Cheong, P. C.-K., Lin, L.-M., & Li, T. G. F. 2020, Classical and Quantum Gravity, 37, 145015, doi: 10.1088/1361-6382/ab8e9c

    2020 doi

  11. [19]

    C.-K., Ng, H

    Cheong, P. C.-K., Ng, H. H.-Y., Lam, A. T.-L., & Li, T. G. F. 2023, ApJS, 267, 38, doi: 10.3847/1538-4365/acd931

    2023 doi

  12. [20]

    C.-K., Pitik, T., Longo Micchi, L

    Cheong, P. C.-K., Pitik, T., Longo Micchi, L. F., & Radice, D. 2025, ApJL, 978, L38, doi: 10.3847/2041-8213/ada1cc

    2025 doi

  13. [21]

    C.-K., Pong, D

    Cheong, P. C.-K., Pong, D. Y. T., Yip, A. K. L., & Li, T. G. F. 2022, ApJS, 261, 22, doi: 10.3847/1538-4365/ac6cec

    2022 doi

  14. [22]

    M., Chatzopoulos, E., Arnett, W

    Couch, S. M., Chatzopoulos, E., Arnett, W. D., & Timmes, F. X. 2015, ApJL, 808, L21, doi: 10.1088/2041-8205/808/1/L21

    2015 doi

  15. [23]

    M., Warren, M

    Couch, S. M., Warren, M. L., & O’Connor, E. P. 2020, ApJ, 890, 127, doi: 10.3847/1538-4357/ab609e

    2020 doi

  16. [24]

    2016, ApJ, 818, 124, doi: 10.3847/0004-637X/818/2/124

    Ugliano, M. 2016, ApJ, 818, 124, doi: 10.3847/0004-637X/818/2/124

    2016 doi

  17. [25]

    1989, Hydrodynamics and nuclaer burning, Max-Planck-Institut für Physik und Astrophysik München: MPA (Max-Planck-Inst

    Fryxell, B., Müller, E., & Arnett, D. 1989, Hydrodynamics and nuclaer burning, Max-Planck-Institut für Physik und Astrophysik München: MPA (Max-Planck-Inst. für Physik und Astrophysik). https://books.google.com/books?id=4LQhtwAACAAJ

    1989

  18. [26]

    2025, ApJ, 981, 119, doi: 10.3847/1538-4357/adb0b8

    Shibata, M. 2025, ApJ, 981, 119, doi: 10.3847/1538-4357/adb0b8

    2025 doi

  19. [27]

    W., & O’Connor, E

    Gogilashvili, M., Murphy, J. W., & O’Connor, E. P. 2023, MNRAS, 524, 4109, doi: 10.1093/mnras/stad2155

    2023 doi

  20. [28]

    T., et al

    Gronow, S., Collins, C., Ohlmann, S. T., et al. 2020, A&A, 635, A169, doi: 10.1051/0004-6361/201936494

    2020 doi

  21. [29]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  22. [30]

    A., Hix, W

    Harris, J. A., Hix, W. R., Chertkow, M. A., et al. 2017, ApJ, 843, 2, doi: 10.3847/1538-4357/aa76de

    2017 doi

  23. [31]

    L., & Duffell, P

    Hasenour, D. L., & Duffell, P. C. 2025, ApJ, 981, 63, doi: 10.3847/1538-4357/adaeb0

    2025 doi

  24. [32]

    R., & Thielemann, F.-K

    Hix, W. R., & Thielemann, F.-K. 1996, ApJ, 460, 869, doi: 10.1086/177016

    1996 doi

  25. [33]

    R., & Thielemann, F

    Hix, W. R., & Thielemann, F. K. 1999, Journal of Computational and Applied Mathematics, 109, 321, doi: 10.48550/arXiv.astro-ph/9906478

    1999 doi arXiv

  26. [34]

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

    2007 doi

  27. [35]

    V., & Ciolfi, R

    Kastaun, W., Kalinani, J. V., & Ciolfi, R. 2021, PhRvD, 103, 023018, doi: 10.1103/PhysRevD.103.023018

    2021 doi

  28. [36]

    M., & Swesty, D

    Lattimer, J. M., & Swesty, D. F. 1991, NuPhA, 535, 331, doi: 10.1016/0375-9474(91)90452-C

    1991 doi

  29. [37]

    C., Chu, M

    Leung, S. C., Chu, M. C., & Lin, L. M. 2015, MNRAS, 454, 1238, doi: 10.1093/mnras/stv1923

    2015 doi

  30. [38]

    Lippuner, J., & Roberts, L. F. 2017, ApJS, 233, 18, doi: 10.3847/1538-4365/aa94cb

    2017 doi

  31. [39]

    2006, A&A, 445, 273, doi: 10.1051/0004-6361:20052840

    Buras, R. 2006, A&A, 445, 273, doi: 10.1051/0004-6361:20052840

    2006 doi

  32. [40]

    2020, PhRvD, 101, 104007, doi: 10.1103/PhysRevD.101.104007

    Mewes, V., Zlochower, Y., Campanelli, M., et al. 2020, PhRvD, 101, 104007, doi: 10.1103/PhysRevD.101.104007

    2020 doi

  33. [41]

    J., Baumgarte, T

    Montero, P. J., Baumgarte, T. W., & Müller, E. 2014, PhRvD, 89, 084043, doi: 10.1103/PhysRevD.89.084043

    2014 doi

  34. [42]

    J., Janka, H.-T., & Müller, E

    Montero, P. J., Janka, H.-T., & Müller, E. 2012, ApJ, 749, 37, doi: 10.1088/0004-637X/749/1/37

    2012 doi

  35. [43]

    1986, A&A, 162, 103

    Mueller, E. 1986, A&A, 162, 103

    1986

  36. [44]

    2014, ApJ, 782, 91, doi: 10.1088/0004-637X/782/2/91 Navó, G., Reichert, M., Obergaulinger, M., & Arcones, A

    Nakamura, K., Takiwaki, T., Kotake, K., & Nishimura, N. 2014, ApJ, 782, 91, doi: 10.1088/0004-637X/782/2/91 Navó, G., Reichert, M., Obergaulinger, M., & Arcones, A. 2023, ApJ, 951, 112, doi: 10.3847/1538-4357/acd640

    2014 doi

  37. [45]

    H.-Y., Cheong, P

    Ng, H. H.-Y., Cheong, P. C.-K., Lam, A. T.-L., & Li, T. G. F. 2024, ApJS, 272, 9, doi: 10.3847/1538-4365/ad2fbd O’Connor, E. 2015, ApJS, 219, 24, doi: 10.1088/0067-0049/219/2/24 O’Connor, E., & Ott, C. D. 2010, Classical and Quantum Gravity, 27, 114103, doi: 10.1088/0264-9381/...

    2024 doi

  38. [46]

    L., & Messer, O

    Papatheodore, T. L., & Messer, O. E. B. 2014, ApJ, 782, 12, doi: 10.1088/0004-637X/782/1/12

    2014 doi

  39. [47]

    2005, Journal of Scientific computing, 25, 129

    Pareschi, L., & Russo, G. 2005, Journal of Scientific computing, 25, 129

    2005

  40. [48]

    2015, ApJ, 806, 275, doi: 10.1088/0004-637X/806/2/275

    Perego, A., Hempel, M., Fröhlich, C., et al. 2015, ApJ, 806, 275, doi: 10.1088/0004-637X/806/2/275

    2015 doi

  41. [49]

    Flannery, B. P. 1996, Numerical recipes in Fortran 90, Vol. 2 (Cambridge university press Cambridge)

    1996

  42. [50]

    Dolence, J. C. 2017, ApJ, 850, 43, doi: 10.3847/1538-4357/aa92c5

    2017 doi

  43. [51]

    Rampp, M., & Janka, H. T. 2002, A&A, 396, 361, doi: 10.1051/0004-6361:20021398

    2002 doi

  44. [52]

    2000, Atomic Data and Nuclear Data Tables, 75, 1, doi: 10.1006/adnd.2000.0834

    Rauscher, T., & Thielemann, F.-K. 2000, Atomic Data and Nuclear Data Tables, 75, 1, doi: 10.1006/adnd.2000.0834

    2000 doi

  45. [53]

    2023, ApJS, 268, 66, doi: 10.3847/1538-4365/acf033

    Reichert, M., Winteler, C., Korobkin, O., et al. 2023, ApJS, 268, 66, doi: 10.3847/1538-4365/acf033

    2023 doi

  46. [54]

    A., Hix, W

    Sandoval, M. A., Hix, W. R., Messer, O. E. B., Lentz, E. J., & Harris, J. A. 2021, ApJ, 921, 113, doi: 10.3847/1538-4357/ac1d49

    2021 doi

  47. [55]

    R., Timmes, F

    Seitenzahl, I. R., Timmes, F. X., Marin-Laflèche, A., et al. 2008, ApJL, 685, L129, doi: 10.1086/592501

    2008 doi

  48. [56]

    R., Townsley, D

    Seitenzahl, I. R., Townsley, D. M., Peng, F., & Truran, J. W. 2009, Atomic Data and Nuclear Data Tables, 95, 96, doi: 10.1016/j.adt.2008.08.001

    2009 doi

  49. [57]

    M., Mösta, P., Desai, D., & Wu, S

    Siegel, D. M., Mösta, P., Desai, D., & Wu, S. 2018, ApJ, 859, 71, doi: 10.3847/1538-4357/aabcc5

    2018 doi

  50. [58]

    W., Hempel, M., & Fischer, T

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

    2013 doi

  51. [59]

    1968, SIAM Journal on Numerical Analysis, 5, 506, doi: 10.1137/0705041

    Strang, G. 1968, SIAM Journal on Numerical Analysis, 5, 506, doi: 10.1137/0705041

    1968 doi

  52. [60]

    Janka, H. T. 2016, ApJ, 821, 38, doi: 10.3847/0004-637X/821/1/38

    2016 doi

  53. [61]

    Timmes, F. X. 1999, ApJS, 124, 241, doi: 10.1086/313257

    1999 doi

  54. [62]

    X., Hoffman, R

    Timmes, F. X., Hoffman, R. D., & Woosley, S. E. 2000, ApJS, 129, 377, doi: 10.1086/313407

    2000 doi

  55. [63]

    X., & Swesty, F

    Timmes, F. X., & Swesty, F. D. 2000, ApJS, 126, 501, doi: 10.1086/313304

    2000 doi

  56. [64]

    J., Smith, B

    Turk, M. J., Smith, B. D., Oishi, J. S., et al. 2011, The Astrophysical Journal Supplement Series, 192, 9, doi: 10.1088/0067-0049/192/1/9

    2011 doi

  57. [65]

    2017, PhRvD, 96, 083016, doi: 10.1103/PhysRevD.96.083016

    Umeda, H. 2017, PhRvD, 96, 083016, doi: 10.1103/PhysRevD.96.083016

    2017 doi

  58. [66]

    2012, ApJ, 757, 69, doi: 10.1088/0004-637X/757/1/69 van Leer, B

    Ugliano, M., Janka, H.-T., Marek, A., & Arcones, A. 2012, ApJ, 757, 69, doi: 10.1088/0004-637X/757/1/69 van Leer, B. 1974, Journal of Computational Physics, 14, 361, doi: 10.1016/0021-9991(74)90019-9

    2012 doi

  59. [67]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2 Wes McKinney. 2010, in Proceedings of the 9th Python in Science Conference, ed. Stéfan van der Walt & Jarrod Millman, 56 – 61, doi: 10.25080/Majora-92bf1922-00a

    2020 doi

  60. [68]

    2017, ApJ, 842, 13, doi: 10.3847/1538-4357/aa72de

    Wongwathanarat, A., Janka, H.-T., Müller, E., Pllumbi, E., & Wanajo, S. 2017, ApJ, 842, 13, doi: 10.3847/1538-4357/aa72de

    2017 doi

  61. [69]

    E., Arnett, W

    Woosley, S. E., Arnett, W. D., & Clayton, D. D. 1973, ApJS, 26, 231, doi: 10.1086/190282

    1973 doi

  62. [70]

    E., & Heger, A

    Woosley, S. E., & Heger, A. 2007, PhR, 442, 269, doi: 10.1016/j.physrep.2007.02.009

    2007 doi

  63. [71]

    E., & Weaver, T

    Woosley, S. E., & Weaver, T. A. 1995, ApJS, 101, 181, doi: 10.1086/192237

    1995 doi

  64. [72]

    2024, ApJ, 966, 150, doi: 10.3847/1538-4357/ad3441

    Zingale, M., Chen, Z., Rasmussen, M., et al. 2024, ApJ, 966, 150, doi: 10.3847/1538-4357/ad3441

    2024 doi

  65. [73]

    Zingale, M., & Katz, M. P. 2015, ApJS, 216, 31, doi: 10.1088/0067-0049/216/2/31

    2015 doi

  66. [74]

    P., Bell, J

    Zingale, M., Katz, M. P., Bell, J. B., et al. 2019, ApJ, 886, 105, doi: 10.3847/1538-4357/ab4e1d

    2019 doi

Pith tools

Reviewed May 18, 2026 · model on record in the stance chip above.