Related papers: Improving nuclear data evaluations with predictive…

The concentration of radiation-induced point defects in general materials under irradiation is commonly described by the point defect kinetics equations based on rate theory. However, the parametric uncertainty in describing the rate…

Trained neural networks (NN) are attractive as surrogate models to replace costly calculations in physical simulations, but are often unknowingly applied to states not adequately represented in the training dataset. We present the novel…

From tabulated nuclear and degenerate equations of state to photon and neutrino opacities, to nuclear reaction rates: tabulated data is ubiquitous in computational astrophysics. The dynamic range that must be covered by these tables…

Mathematical models of the human heart are increasingly playing a vital role in understanding the working mechanisms of the heart, both under healthy functioning and during disease. The aim is to aid medical practitioners diagnose and treat…

Information about the physical properties of astrophysical objects cannot be measured directly but is inferred by interpreting spectroscopic observations in the context of atomic physics calculations. Ratios of emission lines, for example,…

We introduce a novel method for studying systematic trends in nuclear reaction data using generative adversarial networks. Libraries of nuclear cross section evaluations exhibit intricate systematic trends across the nuclear landscape, and…

Effects of difference in the spin and parity distributions for the surrogate and neutron-induced reactions are investigated. Without assuming specific (schematic) spin-parity distributions, it was found that the surrogate ratio method can…

The quantification of uncertainties of computer simulations due to input parameter uncertainties is paramount to assess a model's credibility. For computationally expensive simulations, this is often feasible only via surrogate models that…

A thorough understanding of neutrino-nucleus interactions physics is crucial to achieving precision goals in broader neutrino physics programs. The complexity of nuclei comprising the detectors and limited understanding of their weak…

Surrogate models (including deep neural networks and other machine learning algorithms in supervised learning) are capable of approximating arbitrarily complex, high-dimensional input-output problems in science and engineering, but require…

Explainability of black-box machine learning models is crucial, in particular when deployed in critical applications such as medicine or autonomous cars. Existing approaches produce explanations for the predictions of models, however, how…

For the reliable analysis and modelling of astrophysical, laser-produced and fusion plasmas, atomic data are required for a number of parameters, including energy levels, radiative rates and electron impact excitation rates. Such data are…

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We…

A small number of naturally occurring, proton-rich nuclides (the p-nuclei) cannot be made in the s- and r-process. Their origin is not well understood. Massive stars can produce p-nuclei through photodisintegration of pre-existing…

Exascale computing could soon enable a predictive theory of nuclear structure and reactions rooted in the Standard Model, with quantifiable and systematically improvable uncertainties. Such a predictive theory will help exploit experiments…

Many of the fundamental questions in astrophysics can be addressed using spectroscopic observations of photoionized cosmic plasmas. However, the reliability of the inferred astrophysics depends on the accuracy of the underlying atomic data…

Photon-induced reactions play a key role in the nucleosynthesis of rare neutron-deficient p-nuclei. The paper focuses on (gamma,alpha), (gamma,p), and (gamma,n) reactions which define the corresponding p-process path. The relation between…

Nuclear yields are powerful probes of supernova explosions, their engines and their progenitors. In addition, as we improve our understanding of these explosions, we can use nuclear yields to probe dense matter and neutrino physics, both of…

The neutron capture cross section for $^{90}Zr(n, \gamma)$ has recently been determined using surrogate $^{92}Zr(p, d\gamma)$ data and nuclear reaction theory. That work employed an approximate fitting method based on Bayesian Monte Carlo…

Atomic physics techniques for the determination of ground-state properties of radioactive isotopes are very sensitive and provide accurate masses, binding energies, Q-values, charge radii, spins, and electromagnetic moments. Many fields in…