Martínez-Pinedo, Gabriel ; Langanke, Karlheinz (2022)
Shell Model Applications in Nuclear Astrophysics.
In: Physics, 2022, 4 (2)
doi: 10.26083/tuprints-00022192
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | Shell Model Applications in Nuclear Astrophysics |
Language: | English |
Date: | 5 September 2022 |
Place of Publication: | Darmstadt |
Year of primary publication: | 2022 |
Publisher: | MDPI |
Journal or Publication Title: | Physics |
Volume of the journal: | 4 |
Issue Number: | 2 |
DOI: | 10.26083/tuprints-00022192 |
Corresponding Links: | |
Origin: | Secondary publication DeepGreen |
Abstract: | In recent years, shell model studies have significantly contributed in improving the nuclear input, required in simulations of the dynamics of astrophysical objects and their associated nucleosynthesis. This review highlights a few examples such as electron capture rates and neutrino-nucleus cross sections, important for the evolution and nucleosynthesis of supernovae. For simulations of rapid neutron-capture (r-process) nucleosynthesis, shell model studies have contributed to an improved understanding of half lives of neutron-rich nuclei with magic neutron numbers and of the nuclear level densities and γ-strength functions that are both relevant for neutron capture rates. |
Uncontrolled Keywords: | shell model, core-collapse supernova, r-process nucleosynthesis, neutrino–nucleus reactions, electron capture |
Status: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-221927 |
Additional Information: | This paper is an extended and updated version of the contribution to the 11th International Spring Seminar on Nuclear Physics: Shell Model and Nuclear Structure—Achievements of the Past Two Decades, Ischia, Italy, 12–16 May 2014. |
Classification DDC: | 500 Science and mathematics > 530 Physics |
Divisions: | 05 Department of Physics > Institute of Nuclear Physics > Theoretische Kernphysik |
Date Deposited: | 05 Sep 2022 13:06 |
Last Modified: | 14 Nov 2023 19:05 |
SWORD Depositor: | Deep Green |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/22192 |
PPN: | 498943534 |
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