Related papers: Automated Resonance Identification in Nuclear Data…
The purpose of the present paper is to clarify, as far as it is possible, the overall picture of experimental results in the field of non-conventional phenomena in nuclear matter published in scientific literature, accumulated in the last…
We introduce a Bayesian protocol based on artificial neural networks that is suitable for modeling inclusive electron-nucleus scattering on a variety of nuclear targets with quantified uncertainties. Unlike previous applications in the…
The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk…
The measured and evaluated excitation functions are fundamental quantities that affect the accuracy of all calculations in nuclear applications. Some cross sections, such as 14N(n,p)14C, have added value for special applications, as these…
Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e.g. sensors), possibly pre-processed (e.g. data compression), and finally processed remotely to output the result of…
Nonlinear Resonant Ultrasound Spectroscopy (NRUS) experiments that rely on repeated sampling of resonance curves are inherently sensitive to measurement protocol due to evolution of material parameters caused by fast and slow dynamic…
The quantitative description of the effects of nuclear dynamics on the measured neutrino-nucleus cross sections -- needed to reduce the systematic uncertainty of long baseline neutrino oscillation experiments -- involves severe…
Motivated by applications in high-dimensional data analysis where strong signals often stand out easily and weak ones may be indistinguishable from the noise, we develop a statistical framework to provide a novel categorization of the data…
The interpretation of the nuclear cross sections measured using accelerator neutrino beams involve severe difficulties, arising primarily from the average over the incoming neutrino flux. The broad energy distribution of the beam particles…
Superscaling analyses of few-GeV inclusive electron scattering from nuclei are extended to include not only quasielastic processes, but now also into the region where $\Delta$-excitation dominates. It is shown that, with reasonable…
Correspondence identifies relationships among objects via similarities among their components; it is ubiquitous in the analysis of spatial datasets, including images, weather maps, and computational simulations. This paper develops a novel…
In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and source of such perturbations is fundamental for monitoring…
The resonance phenomenon is of central importance in many areas of physics, with particular significance in the study of nuclear structure and reactions. Starting from the classical framework of damped driven oscillations, this text…
Resonant photonuclear isotope detection (RPID) is a nondestructive detection/assay of nuclear isotopes by measuring gamma rays following photonuclear reaction products. Medium-energy wideband photons of 12-16 MeV are used for the…
Accurate prediction of fragmentation cross sections is essential for rare-isotope beam production, planning new-isotope searches, and designing experiments to study the most exotic regions of the nuclear chart. However, existing reaction…
Precision matrices play important roles in many practical applications. Motivated by temporally dependent multivariate data in modern social and scientific studies, we consider the statistical inference of precision matrices for…
We have previously developed for nuclear cross-sections of therapeutic protons a calculation model, which is founded on the collective model as well as a quantum mechanical many particle problem to derive the S matrix and transition…
A new field of research is rapidly expanding at the crossroad between statistical physics, information theory and combinatorial optimization. In particular, the use of cutting edge statistical physics concepts and methods allow one to solve…
A system's internal dynamics and its interaction with the environment can be determined by tracking how external perturbations affect its transition rates between states. Quantitative measurements of these rates are crucial for optimizing…
The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two…