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Shell Model Applications in Nuclear Astrophysics

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
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

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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