Machine learning opportunities for nucleosynthesis studies
Nuclear astrophysics is an interdisciplinary field focused on exploring the impact of nuclear physics on the evolution and explosions of stars and the cosmic creation of the elements. While researchers in astrophysics and in nuclear physics are separately using machine learning approaches to advance...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Astronomy and Space Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspas.2024.1494439/full |
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| author | Michael S. Smith Dan Lu |
| author_facet | Michael S. Smith Dan Lu |
| author_sort | Michael S. Smith |
| collection | DOAJ |
| description | Nuclear astrophysics is an interdisciplinary field focused on exploring the impact of nuclear physics on the evolution and explosions of stars and the cosmic creation of the elements. While researchers in astrophysics and in nuclear physics are separately using machine learning approaches to advance studies in their fields, there is currently little use of machine learning in nuclear astrophysics. We briefly describe the most common types of machine learning algorithms, and then detail their numerous possible uses to advance nuclear astrophysics, with a focus on simulation-based nucleosynthesis studies. We show that machine learning offers novel, complementary, creative approaches to address many important nucleosynthesis puzzles, with the potential to initiate a new frontier in nuclear astrophysics research. |
| format | Article |
| id | doaj-art-e8732ccaeea34a7f953bb189db5b3fce |
| institution | OA Journals |
| issn | 2296-987X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj-art-e8732ccaeea34a7f953bb189db5b3fce2025-08-20T02:19:11ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2024-12-011110.3389/fspas.2024.14944391494439Machine learning opportunities for nucleosynthesis studiesMichael S. Smith0Dan Lu1Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesNuclear astrophysics is an interdisciplinary field focused on exploring the impact of nuclear physics on the evolution and explosions of stars and the cosmic creation of the elements. While researchers in astrophysics and in nuclear physics are separately using machine learning approaches to advance studies in their fields, there is currently little use of machine learning in nuclear astrophysics. We briefly describe the most common types of machine learning algorithms, and then detail their numerous possible uses to advance nuclear astrophysics, with a focus on simulation-based nucleosynthesis studies. We show that machine learning offers novel, complementary, creative approaches to address many important nucleosynthesis puzzles, with the potential to initiate a new frontier in nuclear astrophysics research.https://www.frontiersin.org/articles/10.3389/fspas.2024.1494439/fullnuclear astrophysicsnucleosynthesissimulationsmachine learningneural nets |
| spellingShingle | Michael S. Smith Dan Lu Machine learning opportunities for nucleosynthesis studies Frontiers in Astronomy and Space Sciences nuclear astrophysics nucleosynthesis simulations machine learning neural nets |
| title | Machine learning opportunities for nucleosynthesis studies |
| title_full | Machine learning opportunities for nucleosynthesis studies |
| title_fullStr | Machine learning opportunities for nucleosynthesis studies |
| title_full_unstemmed | Machine learning opportunities for nucleosynthesis studies |
| title_short | Machine learning opportunities for nucleosynthesis studies |
| title_sort | machine learning opportunities for nucleosynthesis studies |
| topic | nuclear astrophysics nucleosynthesis simulations machine learning neural nets |
| url | https://www.frontiersin.org/articles/10.3389/fspas.2024.1494439/full |
| work_keys_str_mv | AT michaelssmith machinelearningopportunitiesfornucleosynthesisstudies AT danlu machinelearningopportunitiesfornucleosynthesisstudies |