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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
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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