Related papers: Extraction of N* information from the limited p(ga…
This paper concerns applications of genetic algorithms and genetic programming to tasks for which it is difficult to find a representation that does not map to a highly complex and discontinuous fitness landscape. In such cases the standard…
The paper presents a data-driven approach to information extraction (viewed as template filling) using the structured language model (SLM) as a statistical parser. The task of template filling is cast as constrained parsing using the SLM.…
In an unitary effective Lagrangian model we develop a unified description of both meson scattering and photon-induced reactions on the nucleon. Adding the photon to an already existing model for meson-nucleon scattering yields both Compton…
This paper considers the problem of kernel regression and classification with possibly unobservable response variables in the data, where the mechanism that causes the absence of information is unknown and can depend on both predictors and…
Predictions of nuclear properties far from measured data are inherently imprecise because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model…
As quantum machine-learning architectures mature, a central challenge is no longer their construction, but identifying the regimes in which they offer practical advantages over classical approaches. In this work, we introduce a framework…
We explain how an analytic continuation we have developed recently is applied to determine the residues of the nucleon resonance poles within a dynamical coupled-channel model of meson-baron reactions. A procedure for evaluating the…
The nuclear norm (NN) has been widely explored in matrix recovery problems, such as Robust PCA and matrix completion, leveraging the inherent global low-rank structure of the data. In this study, we introduce a new modified nuclear norm…
Differential cross-section and single polarization observables in the process gamma p --> K^+ Lambda are investigated within a constituent quark model and a dynamical coupled-channel formalism. The effects of two new nucleon resonances and…
Software Testing is a process to identify the quality and reliability of software, which can be achieved through the help of proper test data. However, doing this manually is a difficult task due to the presence of number of predicate nodes…
We investigate the $pp \to pK^{+}\Lambda$ reaction within an effective Lagrangian model where the contributions to the amplitudes are taken into account within the tree level. The initial interaction between the two nucleons is modeled by…
Propagating nuclear uncertainties to nucleosynthesis simulations is key to understand the impact of theoretical uncertainties on the predictions, especially for processes far from the stability region, where nuclear properties are scarcely…
Global and national efforts to deliver high-quality nuclear data to users have a broad impact across applications such as national security, reactor operation, basic science, medical fields, and more. Cross section evaluation is a large…
In this review we discuss the present status of strange nuclear physics, with special attention to the weak decay of Lambda hypernuclei. The models proposed for the evaluation of the Lambda decay widths are summarized and their results are…
Machine learning is applied to derive microscopically parameters of the interacting boson model for nuclear spectroscopy. A physics-guided neural network is proposed, which is trained to map the potential energy landscapes that are…
Nuclear attenuation of the two-hadron system is considered in the string model. The two-scale model and its improved version with two different choices of constituent formation time and sets of parameters obtained earlier for the single…
A common problem in physics is to fit regression data by a parametric class of functions, and to decide whether a certain functional form allows for a good fit of the data. Common goodness of fit methods are based on the calculation of the…
We present development of a genetic algorithm for fitting potential energy curves of diatomic molecules to experimental data. Our approach does not involve any functional form for fitting, which makes it a general fitting procedure. In…
Calibration refers to the estimation of unknown parameters which are present in computer experiments but not available in physical experiments. An accurate estimation of these parameters is important because it provides a scientific…
The Genetic Algorithm is a heuristic that can be used to produce model independent solutions to an optimization problem, thus making it ideal for use in cosmology and more specifically in the analysis of type Ia supernovae data. In this…