Related papers: Improving nuclear data evaluations with predictive…
Nucleosynthesis beyond Fe poses additional challenges not encountered when studying astrophysical processes involving light nuclei. Generally higher temperatures and nuclear level densities lead to stronger contributions of transitions on…
Reactions are an important tool to study nuclear structure and for extracting reactions relevant for astrophysics. In this paper we focus on deuteron induced reactions which can provide information on neutron shell evolution as well as…
Compound-nuclear processes play an important role for nuclear physics applications and are crucial for our understanding of the nuclear many-body problem. Despite intensive interest in this area, some of the available theoretical…
The quest to comprehend how nuclear processes influence astrophysical phenomena is driving experimental and theoretical research programs worldwide. One of the main goals in nuclear astrophysics is to understand how energy is generated in…
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…
Recent improvements and remaining problems in the prediction of thermonuclear rates are reviewed. The main emphasis is on statistical model calculations, but the challenge to include direct reactions close to the driplines is also briefly…
This paper presents a physics and data co-driven surrogate modeling method for efficient rare event simulation of civil and mechanical systems with high-dimensional input uncertainties. The method fuses interpretable low-fidelity physical…
Uncertainty quantification has emerged as a rapidly growing field in nuclear science. Theoretical predictions of physical observables often involve extrapolations to regions that are poorly constrained by laboratory experiments and…
Neutron capture cross sections of unstable isotopes are important for neutron induced nucleosynthesis as well as for technological applications. A combination of a radioactive beam facility, an ion storage ring and a high flux reactor would…
Photo-induced reaction cross section data are of importance for a variety of current or emerging applications, such as radiation shielding design and radiation transport analyses, calculations of absorbed dose in the human body during…
We consider a class of stochastic programming problems where the implicitly decision-dependent random variable follows a nonparametric regression model with heteroscedastic error. The Clarke subdifferential and surrogate functions are not…
Accurate nuclear reaction rates are needed for primordial nucleosynthesis and hydrostatic burning in stars. The relevant reactions are extremely difficult to measure directly in the laboratory at the small astrophysical energies. In recent…
This review focuses on nuclear reactions in astrophysics and, more specifically, on reactions with light ions (nucleons and alpha particles) proceeding via the strong interaction. It is intended to present the basic definitions essential…
The relation between the neutron background in neutron capture measurements and the neutron sensitivity related to the experimental setup is examined. It is pointed out that a proper estimate of the neutron background may only be obtained…
Rigorous statistical methods for estimating thermonuclear reaction rates and nucleosynthesis are becoming increasingly established in nuclear astrophysics. The main challenge being faced is that experimental reaction rates are highly…
When a nuclear species (e.g., a nucleon or a deuteron nucleus) propagating freely is made to collide with a target nucleus, its trajectory is modified by exchanging variable amounts of energy, mass, linear and angular momentum with the…
Nuclear charge radii constitute a physical observable of growing significance across multiple subdisciplines of physics and related fields. Their determination relies on a combination of complementary experimental techniques and advanced…
Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control, and establishing appropriate designs for future…
In networked dynamical systems, inferring governing parameters is crucial for predicting nodal dynamics, such as gene expression levels, species abundance, or population density. While many parameter estimation techniques rely on…
Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical…