MCViNE -- An object oriented Monte Carlo neutron ray tracing simulation package

MCViNE (Monte-Carlo VIrtual Neutron Experiment) is a versatile Monte Carlo (MC) neutron ray-tracing program that provides researchers with tools for performing computer modeling and simulations that mirror real neutron scattering experiments. By adopting modern software engineering practices such as using composite and visitor design patterns for representing and accessing neutron scatterers, and using recursive algorithms for multiple scattering, MCViNE is flexible enough to handle sophisticated neutron scattering problems including, for example, neutron detection by complex detector systems, and single and multiple scattering events in a variety of samples and sample environments. In addition, MCViNE can take advantage of simulation components in linear-chain-based MC ray tracing packages widely used in instrument design and optimization, as well as NumPy-based components that make prototypes useful and easy to develop. These developments have enabled us to carry out detailed simulations of neutron scattering experiments with non-trivial samples in time-of-flight inelastic instruments at the Spallation Neutron Source. Examples of such simulations for powder and single-crystal samples with various scattering kernels, including kernels for phonon and magnon scattering, are presented. With simulations that closely reproduce experimental results, scattering mechanisms can be turned on and off to determine how they contribute to the measured scattering intensities, improving our understanding of the underlying physics.Comment: 34 pages, 14 figure

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables

Generic guide concepts for the European Spallation Source

The construction of the European Spallation Source (ESS) faces many challenges from the neutron beam transport point of view: The spallation source is specified as being driven by a 5 MW beam of protons, each with 2 GeV energy, and yet the requirements in instrument background suppression relative to measured signal vary between 10$^{-6}$ and 10$^{-8}$. The energetic particles, particularly above 20 MeV, which are expected to be produced in abundance in the target, have to be filtered in order to make the beamlines safe, operational and provide good quality measurements with low background. We present generic neutron guides of short and medium length instruments which are optimized for good performance at minimal cost. Direct line of sight to the source is avoided twice, with either the first point out of line of sight or both being inside the bunker (20\,m) to minimize shielding costs. These guide geometries are regarded as a baseline to define standards for instruments to be constructed at ESS. They are used to find commonalities and develop principles and solutions for common problems. Lastly, we report the impact of employing the over-illumination concept to mitigate losses from random misalignment passively, and that over-illumination should be used sparingly in key locations to be effective. For more widespread alignment issues, a more direct, active approach is likely to be needed

A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators

We propose a hierarchical Bayesian model and state-of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neutron source from the data detected by an organic scintillator. Inferring neutron spectra is important for several applications, including nonproliferation and nuclear security, as it allows the discrimination of fission sources in special nuclear material (SNM) from other types of neutron sources based on the differences of the emitted neutron spectra. Organic scintillators interact with neutrons mostly via elastic scattering on hydrogen nuclei and therefore partially retain neutron energy information. Consequently, the neutron spectrum can be derived through deconvolution of the measured light output spectrum and the response functions of the scintillator to monoenergetic neutrons. The proposed approach is compared to three existing methods using simulated data to enable controlled benchmarks. We consider three sets of detector responses. One set corresponds to a 2.5 MeV monoenergetic neutron source and two sets are associated with (energy-wise) continuous neutron sources ($^{252}$Cf and $^{241}$AmBe). Our results show that the proposed method has similar or better unfolding performance compared to other iterative or Tikhonov regularization-based approaches in terms of accuracy and robustness against limited detection events, while requiring less user supervision. The proposed method also provides a posteriori confidence measures, which offers additional information regarding the uncertainty of the measurements and the extracted information.Comment: 10 page

Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure

Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy2Ti2O7, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.Fil: Samarakoon, Anjana. Oak Ridge National Laboratory; Estados Unidos. Argonne National Laboratory; Estados UnidosFil: Tennant, D. Alan. Oak Ridge National Laboratory; Estados UnidosFil: Ye, Feng. Oak Ridge National Laboratory; Estados UnidosFil: Zhang, Qiang. Oak Ridge National Laboratory; Estados UnidosFil: Grigera, Santiago Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentin

The Future of Neutrino Mass Measurements: Terrestrial, Astrophysical, and Cosmological Measurements in the Next Decade. Highlights of the NuMass 2013 Workshop. Milano, Italy, February 4 - 7, 2013

The third Workshop of the NuMass series ("The Future of Neutrino Mass Measurements: Terrestrial, Astrophysical, and Cosmological Measurements in the Next Decade: NuMass 2013") was held at Dipartimento di Fisica "G. Occhialini, University of Milano-Bicocca in Milano, Italy, on 4-7 February 2013. The goal of this international workshop was to review the status and future of direct and indirect neutrino mass measurements in the laboratory as well as from astrophysical and cosmological observations. This paper collects most of the contributions presented during the Workshop

Next Generation Energy Storage: An Examination of Lignin-based Carbon Composite Anodes for Sodium Ion Batteries through Modeling and Simulation

The current energy market relies heavily on fossil fuel sources; however, we are amidst a momentous shift towards wind, solar, and water based renewable energies. Large-scale energy storage allows renewable energy to be stored and supply the grid with consistent energy despite changing weather conditions. Improvements to large-scale energy storage in terms of cost, safety, and sustainability are crucial to wide-scale adoption. A promising candidate for large-scale energy storage are sodium-ion batteries using hard carbon anodes. Sodium is globally available, cheaper, and more sustainable than lithium, but requires a different anode structure. A sustainable hard carbon anode with excellent Li-ion performance has been manufactured from lignin, a byproduct of the paper and bio-ethanol industries. The carbon composite generated from lignin is composed of nanoscale crystallites dispersed in an amorphous graphene matrix whose structure is highly dependent on manufacturing process; however, the sodium-ion storage mechanisms for these lignin-based hard carbons are not well known. The purpose of the following work is to elucidate the Na-ion storage mechanisms for these lignin-based hard carbons and develop process-structure-property-performance (PSPP) relationships for them so an optimal Na-ion anode can be manufactured. To this end, reactive molecular dynamics simulations of lignin-based carbon composites were conducted with both lithium and sodium to compare the binding energies and mechanisms as well as their respective diffusive properties. It was found that lithium-ions prefer to localize in the hydrogen dense interfacial regions of the carbon composites while sodium prefer to adsorb to the surfaces of graphene fragments as well as the outer faces and edge-intercalation positions of the crystallites. At higher porosity, sodium shows a tendency to aggregate in the porous regions along curved planes of graphene, which gives the Na-ions the highest diffusion rate of all systems studied. To aid in determining the PSPP relationships of LBCCs, synchrotron x-ray scattering was performed, and models were created and refined using the Hierarchical Decomposition of the Radial Distribution Function (HDRDF) technique and software (now highly generalized). PSPP relationships with respect to processing temperature were quantitatively and qualitatively determined for the lignin-based carbon composites