FIGURE 1 - available via license: Creative Commons Attribution 4.0 International
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Reactions of importance for nuclear astrophysics shown on the N-Z plane for stable nuclei (center), and as lists for proton-rich nuclei (right), and neutron-rich nuclei (left).
Source publication
Research into the cosmic synthesis of the elements, the evolution and explosion of stars, the nature of the early Universe, and other important topics in nuclear astrophysics are at the forefront of nuclear science. These studies are motivating laboratory measurements and theoretical calculations that, after significant investments, are pushing the...
Contexts in source publication
Context 1
... strong reactions, interaction probabilities are characterized by cross sections. Nuclear astrophysics studies do not, however, require cross sections of all reaction types at all energies; Figure 1 shows the set of reaction types most predominantly utilized in this field. Noted exceptions to this are the important 12 C + 12 C, 12 C + 16 O, 16 O + 16 O, and similar reactions occurring in carbon and oxygen burning stages of massive stars where the low abundance of light nuclei causes heavy ion reactions to dominate (Nagorcka et al., 1971;Rolfs and Rodney, 1988). ...
Context 2
... exceptions to this are the important 12 C + 12 C, 12 C + 16 O, 16 O + 16 O, and similar reactions occurring in carbon and oxygen burning stages of massive stars where the low abundance of light nuclei causes heavy ion reactions to dominate (Nagorcka et al., 1971;Rolfs and Rodney, 1988). Furthermore, cross sections for different reaction types are needed for nuclei in different locations of the nuclear chart ( Figure 1). For example, neutron captures are important for stable nuclei and those that have a neutron excess, while proton and alpha captures are needed for stable nuclei and those with a proton excess. ...
Citations
Nuclide production cross sections are crucial in nuclear research, development, space exploration, and astrophysical investigations. Despite their importance, limited experimental data availability restricts the practicality of phenomenological approaches to comprehensive cross-section estimation. To address this, we propose a Gaussian process-based machine learning (ML) model capable of transferring knowledge from elements with abundant data to those with limited or no experimental data. Our ML model not only enables comprehensive cross-section estimations for various elements but also demonstrates predictive capabilities akin to physics models, even in regions with scarce training data.
