Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors
One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully coupled stiff ordinary differential equations, making the simulati...
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2025-01-01
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| Online Access: | https://doi.org/10.3847/1538-4365/ade717 |
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| author | Aldana Grichener Mathieu Renzo Wolfgang E. Kerzendorf Rob Farmer Selma E. de Mink Earl Patrick Bellinger Chi-kwan Chan Nutan Chen Ebraheem Farag Stephen Justham |
| author_facet | Aldana Grichener Mathieu Renzo Wolfgang E. Kerzendorf Rob Farmer Selma E. de Mink Earl Patrick Bellinger Chi-kwan Chan Nutan Chen Ebraheem Farag Stephen Justham |
| author_sort | Aldana Grichener |
| collection | DOAJ |
| description | One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully coupled stiff ordinary differential equations, making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof of concept to evaluate its performance. The NNN takes the temperature, density, and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280%–660%, the average atomic and mass numbers by 150%–360%, and the nuclear energy generation by 250%–750%, consistently outperforming the small network across all time steps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100% to 1,000,000%. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN-trained models into stellar evolution codes is promising for facilitating the large-scale generation of core-collapse supernova progenitors with higher physical fidelity. |
| format | Article |
| id | doaj-art-f51f638db2e74c849962a14d143ee6c9 |
| institution | Kabale University |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-f51f638db2e74c849962a14d143ee6c92025-08-20T03:58:18ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127924910.3847/1538-4365/ade717Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova ProgenitorsAldana Grichener0https://orcid.org/0000-0002-2215-1841Mathieu Renzo1https://orcid.org/0000-0002-6718-9472Wolfgang E. Kerzendorf2https://orcid.org/0000-0002-0479-7235Rob Farmer3https://orcid.org/0000-0003-3441-7624Selma E. de Mink4https://orcid.org/0000-0001-9336-2825Earl Patrick Bellinger5https://orcid.org/0000-0003-4456-4863Chi-kwan Chan6https://orcid.org/0000-0001-6337-6126Nutan Chen7https://orcid.org/0000-0001-7571-0742Ebraheem Farag8https://orcid.org/0000-0002-5794-4286Stephen Justham9https://orcid.org/0000-0001-7969-1569Steward Observatory and Department of Astronomy, University of Arizona , 933 North Cherry Avenue, Tucson, AZ 85721, USA ; agrichener@arizona.edu; Max Planck Institute for Astrophysics , Karl-Schwarzschild-Str. 1, 85748 Garching, Germany; Department of Physics, Technion , Haifa, 3200003, IsraelSteward Observatory and Department of Astronomy, University of Arizona , 933 North Cherry Avenue, Tucson, AZ 85721, USA ; agrichener@arizona.eduDepartment of Computational Mathematics, Science, and Engineering, Michigan State University , East Lansing, MI 48824, USA; Department of Physics and Astronomy, Michigan State University , East Lansing, MI 48824, USAMax Planck Institute for Astrophysics , Karl-Schwarzschild-Str. 1, 85748 Garching, GermanyMax Planck Institute for Astrophysics , Karl-Schwarzschild-Str. 1, 85748 Garching, Germany; Ludwig-Maximilians-Universitat Munchen , Geschwister-Scholl-Platz 1, 80539 Munchen, GermanyDepartment of Astronomy, Yale University , New Haven, CT 06511, USASteward Observatory and Department of Astronomy, University of Arizona , 933 North Cherry Avenue, Tucson, AZ 85721, USA ; agrichener@arizona.edu; Data Science Institute, University of Arizona , 1230 N. Cherry Avenue, Tucson, AZ 85721, USA; Program in Applied Mathematics, University of Arizona , 617 North Santa Rita Avenue, Tucson, AZ 85721, USAMachine Learning Research Lab, Volkswagen AG , 38440 Munich, GermanyDepartment of Astronomy, Yale University , New Haven, CT 06511, USAMax Planck Institute for Astrophysics , Karl-Schwarzschild-Str. 1, 85748 Garching, GermanyOne of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully coupled stiff ordinary differential equations, making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof of concept to evaluate its performance. The NNN takes the temperature, density, and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280%–660%, the average atomic and mass numbers by 150%–360%, and the nuclear energy generation by 250%–750%, consistently outperforming the small network across all time steps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100% to 1,000,000%. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN-trained models into stellar evolution codes is promising for facilitating the large-scale generation of core-collapse supernova progenitors with higher physical fidelity.https://doi.org/10.3847/1538-4365/ade717Core-collapse supernovaeMassive starsNucleosynthesis |
| spellingShingle | Aldana Grichener Mathieu Renzo Wolfgang E. Kerzendorf Rob Farmer Selma E. de Mink Earl Patrick Bellinger Chi-kwan Chan Nutan Chen Ebraheem Farag Stephen Justham Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors The Astrophysical Journal Supplement Series Core-collapse supernovae Massive stars Nucleosynthesis |
| title | Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors |
| title_full | Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors |
| title_fullStr | Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors |
| title_full_unstemmed | Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors |
| title_short | Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors |
| title_sort | nuclear neural networks emulating late burning stages in core collapse supernova progenitors |
| topic | Core-collapse supernovae Massive stars Nucleosynthesis |
| url | https://doi.org/10.3847/1538-4365/ade717 |
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