Progress in Nuclear Astrophysics: a multi-disciplinary field with still many open questions

TL;DR

This paper reviews recent advances and ongoing challenges in nuclear astrophysics, highlighting experimental, theoretical, and machine learning developments in understanding stellar nucleosynthesis and related nuclear data needs.

Contribution

It discusses recent experimental progress, theoretical modeling of exotic nuclei, and the application of machine learning in nuclear astrophysics, emphasizing unresolved problems.

Findings

Progress in measuring key nuclear reactions for s- and p-processes

Advances in predicting properties of neutron-rich nuclei for r-process

Introduction of machine learning techniques in nuclear astrophysics

Abstract

Nuclear astrophysics is a multi-disciplinary field with a huge demand for nuclear data. Among its various fields, stellar evolution and nucleosynthesis are clearly the most closely related to nuclear physics. The need for nuclear data for astrophysics applications challenges experimental techniques as well as the robustness and predictive power of present nuclear models. Despite impressive progress for the last years, major problems and puzzles remain. In the present contribution, only a few nuclear astrophysics specific aspects are discussed. These concern some experimental progress related to the measurement of key reactions of relevance for the so-called s-and p-processes of nucleosynthesis, the theoretical effort in predicting nuclear properties of exotic neutron-rich nuclei of interest for the r-process nucleosynthesis, and the recent introduction of machine learning techniques in nuclear astrophysics applications.