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The i-process and CEMP-r/s stars

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arxiv 1505.05500 v2 pith:AZRKG5GF submitted 2015-05-20 astro-ph.SR

classification astro-ph.SR
keywords i-processstarscemp-rsimulationsyieldsabundanceconditionshe-shell
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We investigate whether the anomalous elemental abundance patterns in some of the C-enhanced metal-poor-s+r (CEMP-r/s) stars are consistent with predictions of nucleosynthesis yields from the i-process, a neutron-capture regime at neutron densities intermediate between those typical for the slow (s) and rapid (r) processes. Conditions necessary for the i-process are expected to be met at multiple stellar sites, such as the He-core and He-shell flashes in low-metallicity low-mass stars, super-AGB and post-AGB stars, as well as low-metallicity massive stars. We have found that single-exposure one-zone simulations of the i-process reproduce the abundance patterns in some of the CEMP-r/s stars much better than the model that assumes a superposition of yields from s- and r-process sources. Our previous study of nuclear data uncertainties relevant to the i-process revealed that they could have a significant impact on the i-process yields obtained in our idealized one-zone calculations, leading, for example, to ~0.7dex uncertainty in our predicted [Ba/La] ratio. Recent 3D hydrodynamic simulations of convection driven by a He-shell flash in post-AGB Sakurai's object have discovered a new mode of non-radial instabilities: the Global Oscillation of Shell H-ingestion. This has demonstrated that spherically symmetric stellar evolution simulations cannot be used to accurately model physical conditions for the i-process.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 13 citations worldwide. Full citation record

  1. Classifying metal-poor stars with machine learning using nucleosynthesis calculations

    nucl-th 2025-05 conditional novelty 6.0 of 10

    Machine learning trained on simulated r- and s-process nucleosynthesis patterns classifies metal-poor stars in agreement with conventional labels 87% of the time and suggests some standard classifications are wrong.

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