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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
... rates of charged particle reactions depend exponentially on temperature, with some rates varying by 30 orders of magnitude or more over the 10 7 K-10 10 K temperature range relevant for astrophysical environments. Figure 2 shows a typical example, the rate for the 17 F(p,γ) 18 Ne reaction that plays a critical role in the synthesis of 17 O in nova explosions ( Bardayan et al., 1999). Collections of hundreds to thousands of individual reaction rates-rate "libraries"-are the core nuclear physics input for simulations of astrophysical environments. ...
Context 2
... important processing step is the fitting of temperaturedependent rates to analytical formulae. Such fits are challenging because reaction rates can vary by up to 30 orders of magnitude or more over astrophysical temperature ranges (see, e.g., Figure 2). Fits of rates to the REACLIB analytical formulation are, for example, discussed in Cyburt et al. (2010). ...
Context 3
... some cases, libraries can also be accessed through services that incorporate or manipulate them. For example, the JINA REACLIB library can be accessed through CINA (Smith, 2023), and NACRE and NACRE-II can be accessed through BRUSLIB (Goriely, 2023b). It is not uncommon for website addresses to change, however, as well as for web hosting to be discontinued. ...
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.
