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arxiv: 2510.12978 · v2 · submitted 2025-10-14 · 🌌 astro-ph.IM · astro-ph.HE· nucl-th

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

Patrick Chi-Kit Cheong , Christopher L. Fryer This is my paper

Pith reviewed 2026-05-18 06:59 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.HEnucl-th
keywords nuclear networksGRRMHDcore-collapse supernovaneutrino transportexplosive burningGmunu
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The pith

Coupling nuclear reaction networks to GR radiation-MHD changes supernova composition and shock strength.

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

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.

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.

What carries the argument

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

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.

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.

Figures

Figures reproduced from arXiv: 2510.12978 by Christopher L. Fryer, Patrick Chi-Kit Cheong.

Figure 1
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 … view at source ↗
Figure 2
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. 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 |… view at source ↗
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 lines: exact reference solution. The excellent agreement validates the stellar EoS implementation. 128 and up to lmax = 13 mesh-refinement … view at source ↗
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 not suppressed. Minor offsets in shock position are expected due to slight differences in the evaluation time and numerical treatment. Unlike … view at source ↗
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 for unbound material.1 In the Newtonian limit, equation (32) reduces to the 1 Strictly speaking, the Bernoulli criterion is not the only poss… view at source ↗
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, 56Ni), silicon-group (28Si, 32S, 36Ar, 40Ca), oxygen-group (16O, 20Ne, 24Mg), alpha particle, neutron, and proton. Overplotted lines mark th… view at source ↗
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, the shock is not revived and composition remains largely unchanged. Enhanced heating prolongs shock expansion, but the post-shock matter is d… view at source ↗
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, both the net neutrino heating rate and the mass accretion rate decrease significantly once shock revival is achieved. These quantities exhibit … view at source ↗
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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

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

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.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 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.
axioms (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.

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Forward citations

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Reference graph

Works this paper leans on

74 extracted references · 74 canonical work pages · cited by 1 Pith paper · 2 internal anchors

  1. [1]

    P., Abbott, R., Abbott, T

    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]

    and Ruuth, Steven J

    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]

    2025, Research Notes of the American Astronomical Society, 9, 113, doi: 10.3847/2515-5172/add686

    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

  9. [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

    work page doi:10.3847/0004-637x/818/2/123 2016

  10. [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

    work page doi:10.3847/1538-4365/ab7aff 2020

  11. [11]

    T., & Kifonidis, K

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

    work page doi:10.1051/0004-6361:20053783 2006

  12. [12]

    C., Townsley, D

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

    work page doi:10.1086/510709 2007

  13. [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

    work page doi:10.5281/zenodo.7697899 2023

  14. [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

    work page doi:10.1016/j.jcp.2015.01.031 2015

  15. [15]

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

    work page doi:10.1103/physreve.58.4941 1998

  16. [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

    work page doi:10.3847/1538-4357/ad7825 2024

  17. [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

    work page doi:10.1093/mnras/stab2606 2021

  18. [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

    work page doi:10.1088/1361-6382/ab8e9c 2020

  19. [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

    work page doi:10.3847/1538-4365/acd931 2023

  20. [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

    work page doi:10.3847/2041-8213/ada1cc 2025

  21. [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

    work page doi:10.3847/1538-4365/ac6cec 2022

  22. [22]

    The Astrophysical Journal , author=

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

    work page doi:10.1088/2041-8205/808/1/l21 2015

  23. [23]

    M., Warren, M

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

    work page doi:10.3847/1538-4357/ab609e 2020

  24. [24]

    and Janka, H

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

    work page doi:10.3847/0004-637x/818/2/124 2016

  25. [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

    work page 1989

  26. [26]

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

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

    work page doi:10.3847/1538-4357/adb0b8 2025

  27. [27]

    W., & O’Connor, E

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

    work page doi:10.1093/mnras/stad2155 2023

  28. [28]

    T., Pakmor R., Kromer M., Seitenzahl I

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

    work page doi:10.1051/0004-6361/201936494 2020

  29. [29]

    Harris, 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

    work page doi:10.1038/s41586-020-2649-2 2020

  30. [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

    work page doi:10.3847/1538-4357/aa76de 2017

  31. [31]

    L., & Duffell, P

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

    work page doi:10.3847/1538-4357/adaeb0 2025

  32. [32]

    R., & Thielemann, F.-K

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

    work page doi:10.1086/177016 1996

  33. [33]

    Computational Methods for Nucleosynthesis and Nuclear Energy Generation

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

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.astro-ph/9906478 1999

  34. [34]

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

    work page doi:10.1109/mcse.2007.55 2007

  35. [35]

    V., & Ciolfi, R

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

    work page doi:10.1103/physrevd.103.023018 2021

  36. [36]

    M., & Swesty, D

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

    work page doi:10.1016/0375-9474(91)90452-c 1991

  37. [37]

    C., Chu, M

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

    work page doi:10.1093/mnras/stv1923 2015

  38. [38]

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

    work page doi:10.3847/1538-4365/aa94cb 2017

  39. [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

    work page doi:10.1051/0004-6361:20052840 2006

  40. [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

    work page doi:10.1103/physrevd.101.104007 2020

  41. [41]

    J., Baumgarte, T

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

    work page doi:10.1103/physrevd.89.084043 2014

  42. [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

    work page doi:10.1088/0004-637x/749/1/37 2012

  43. [43]

    1986, A&A, 162, 103

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

    work page 1986

  44. [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

    work page doi:10.1088/0004-637x/782/2/91 2014

  45. [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: 10.1088/0264-9381/27/11/114103 O’Connor, E., Bollig, R., Burrows, A., et al. 2018, Journal of Phys...

    work page doi:10.3847/1538-4365/ad2fbd 2024

  46. [46]

    L., & Messer, O

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

    work page doi:10.1088/0004-637x/782/1/12 2014

  47. [47]

    2005, Journal of Scientific computing, 25, 129

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

    work page 2005

  48. [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

    work page doi:10.1088/0004-637x/806/2/275 2015

  49. [49]

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

    work page 1996

  50. [50]

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

    work page doi:10.3847/1538-4357/aa92c5 2017

  51. [51]

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

    work page doi:10.1051/0004-6361:20021398 2002

  52. [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

    work page doi:10.1006/adnd.2000.0834 2000

  53. [53]

    2023, The Astrophysical Journal Supplement Series, 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

    work page doi:10.3847/1538-4365/acf033 2023

  54. [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

    work page doi:10.3847/1538-4357/ac1d49 2021

  55. [55]

    R., Timmes, F

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

    work page doi:10.1086/592501 2008

  56. [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

    work page doi:10.1016/j.adt.2008.08.001 2009

  57. [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

    work page doi:10.3847/1538-4357/aabcc5 2018

  58. [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

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

  59. [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

    work page doi:10.1137/0705041 1968

  60. [60]

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

    work page internal anchor Pith review doi:10.3847/0004-637x/821/1/38 2016

  61. [61]

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

    work page doi:10.1086/313257 1999

  62. [62]

    X., Hoffman, R

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

    work page doi:10.1086/313407 2000

  63. [63]

    2000 , month =

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

    work page doi:10.1086/313304 2000

  64. [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

    work page doi:10.1088/0067-0049/192/1/9 2011

  65. [65]

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

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

    work page doi:10.1103/physrevd.96.083016 2017

  66. [66]

    ApJ , author =

    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

    work page doi:10.1088/0004-637x/757/1/69 2012

  67. [67]

    Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J

    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

    work page doi:10.1038/s41592-019-0686-2 2020

  68. [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

    work page doi:10.3847/1538-4357/aa72de 2017

  69. [69]

    E., Arnett, W

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

    work page doi:10.1086/190282 1973

  70. [70]

    E., & Heger, A

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

    work page doi:10.1016/j.physrep.2007.02.009 2007

  71. [71]

    E., & Weaver, T

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

    work page doi:10.1086/192237 1995

  72. [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

    work page doi:10.3847/1538-4357/ad3441 2024

  73. [73]

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

    work page doi:10.1088/0067-0049/216/2/31 2015

  74. [74]

    P., Bell, J

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

    work page doi:10.3847/1538-4357/ab4e1d 2019