Related papers: Statistical framework for nuclear parameter uncert…
The rapid neutron-capture process ($r$-process) has for the first time been confirmed to take place in a neutron-star merger event. A detailed understanding of the rapid neutron-capture process is one of the holy grails in nuclear…
Nucleation processes, through which a new structure progressively forms within a pre-existing homogeneous phase, are fundamental in materials science, but are also typically non-trivial to elucidate. Cases in which to nucleate are defects…
We present a technique for estimating the number of future neutrinoless double-beta decay results using several distinct nuclei to optimize the physics reach of upcoming experiments. We use presently available matrix element calculations…
Computing systems interacting with real-world processes must safely and reliably process uncertain data. The Monte Carlo method is a popular approach for computing with such uncertain values. This article introduces a framework for…
The Best Estimate plus Uncertainty (BEPU) approach for nuclear systems modeling and simulation requires that the prediction uncertainty must be quantified in order to prove that the investigated design stays within acceptance criteria. A…
Accelerator-based neutrino oscillation experiments have the potential to revolutionise our understanding of fundamental physics, offering an opportunity to characterise charge-parity violation in the lepton sector; to determine the neutrino…
Any simulation of the r-process is affected by uncertainties in our present knowledge of nuclear physics quantities and astrophysical conditions. It is common to quantify the impact of these uncertainties through a global sensitivity…
Theoretical predictions need quantified uncertainties for a meaningful comparison to experimental results. This is an idea which presently permeates the field of theoretical nuclear physics. In light of the recent progress in estimating…
The simulation of heavy element nucleosynthesis requires input from yet-to-be-measured nuclear properties. The uncertainty in the values of these off-stability nuclear properties propagates to uncertainties in the predictions of elemental…
Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape…
Several thousand tracers from a 2D model of a thermonuclear supernova were used in a Monte Carlo post-processing approach to determine p-nuclide abundance uncertainties originating from nuclear physics uncertainties in the reaction rates.
Bayesian analysis often concerns an evaluation of models with different dimensionality as is necessary in, for example, model selection or mixture models. To facilitate this evaluation, transdimensional Markov chain Monte Carlo (MCMC)…
We evaluated the uncertainty relevant to s-process nucleosynthesis using a Monte-Carlo centred approach. We are based on a realistic and general prescription of temperature dependent uncertainty for the reactions. We considered massive…
The influence of the uncertainties of the equation of state empirical parameters on the neutron stars crust-core phase transition is explored within a meta-modeling approach, in which the energy per particle is expanded as a Taylor series…
Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the…
Proton-rich nuclei, the so-called p-nuclei, are made in photodisintegration processes in outer shells of massive stars in the course of the final supernova explosion. Nuclear uncertainties in the production of these nuclei have been…
The precise measurement of neutrino properties is among the highest priorities in fundamental particle physics, involving many experiments worldwide. Since the experiments rely on the interactions of neutrinos with bound nucleons inside…
In the framework of BEPU (Best Estimate plus Uncertainty) methodology, the uncertainties involved in the simulations must be quantified to prove that the investigated design is acceptable. The output uncertainties are usually calculated by…
Modeling of X-ray pulse profiles from millisecond pulsars offers a promising method of inferring the mass-to-radius ratios of neutron stars. Recent observations with NICER resulted in measurements of radii for three neutron stars using this…
Characterizing uncertainty is a common issue in nuclear measurement and has important implications for reliable physical discovery. Traditional methods are either insufficient to cope with the heterogeneous nature of uncertainty or…