Related papers: Neutron Yield Predictions with Artificial Neural N…
Developing high-precision models of the nuclear force and propagating the associated uncertainties in quantum many-body calculations of nuclei and nuclear matter remain key challenges for ab initio nuclear theory. In the present work we…
Neutron noise in nuclear power reactors refers to the small fluctuations around the average neutron flux at steady state resulting from time-dependent perturbations inside the core. The neutron noise equations in the frequency domain can be…
Ray tracing algorithms that compute pulse profiles from rotating neutron stars are essential tools for constraining neutron-star properties with data from missions such as NICER. However, the high computational cost of these simulations…
The neutron emission in projectile fragmentation at relativistic energies was studied with the Large-Area-Neutron-Detector LAND coupled to the ALADIN forward spectrometer at the GSI Schwerionen-Synchrotron (SIS). Stable 124Sn and…
Adversarial examples are considered a serious issue for safety critical applications of AI, such as finance, autonomous vehicle control and medicinal applications. Though significant work has resulted in increased robustness of systems to…
For reactor neutrino experiments including the next--generation experiments will be adopting the liquid scintillator technique, criteria and time to select neutrino--induced inverse beta decay events from the background events need to be…
In order to optimise the design of space instruments making use of detection materials with low atomic numbers, an understanding of the atmospheric neutron environment and its dependencies on time and position is needed. To produce a simple…
We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The solution of a traditional regression problem is then obtained…
We discuss methods to extract neutrino oscillation parameters based on the directly observable quantities, without reconstruction of neutrino energy. The distributions of muon energies and production angles are compared to Monte Carlo…
Neutrons can induce background events in underground experiments looking for rare processes. Neutrons in a MeV range are produced in radioactive decays via spontaneous fission and (alpha, n) reactions, and by cosmic rays. Neutron fluxes…
Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo…
Crop yield prediction typically involves the utilization of either theory-driven process-based crop growth models, which have proven to be difficult to calibrate for local conditions, or data-driven machine learning methods, which are known…
Purpose: Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity…
Modern nuclear data evaluation increasingly requires not only accurate scattering calculations, but also efficient methods for uncertainty quantification and parameter optimization, tasks that benefit from differentiable solvers amenable to…
Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertainty-aware deep kernel learning model which permits the…
Spallation neutron production in proton induced reactions on Al, Fe, Zr, W, Pb and Th targets at 1.2 GeV and on Fe and Pb at 0.8, and 1.6 GeV measured at the SATURNE accelerator in Saclay is reported. The experimental double-differential…
The Auxiliary Field Diffusion Monte Carlo method has been applied to simulate droplets of 7 and 8 neutrons. Results for realistic nucleon-nucleon interactions, which include tensor, spin--orbit and three--body forces, plus a standard…
In recent years, several successful applications of the Artificial Neural Networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry, meteorology, and other fields of science. A major goal of…
Ion-ion collisions at relativistic energies have been shown recently to be a promising technique for the production of hypernuclei. In this article, we further investigate the production of light $\Lambda$ hypernuclei by use of a hybrid…
We present a novel approach using neural networks to recover X-ray spectral model parameters and quantify uncertainties, balancing accuracy and computational efficiency against traditional frequentist and Bayesian methods. Frequentist…