Related papers: McStas and Mantid integration
Monochromator and analyzer systems that rely on bent single crystals are in use throughout the neutron scattering community. We here introduce a new component to the neutron simulation software package McStas, that simulates these bent…
We present an overview of, and an introduction to, the general-purpose neutron simulation package McStas. We present the basic principles behind Monte Carlo ray-tracing simulations of neutrons performed in the package and present a few…
An open source software package for modelling thermal neutron transport is presented. The code facilitates Monte Carlo-based transport simulations and focuses in the initial release on interactions in both mosaic single crystals as well as…
In this work we present the development of small angle scattering components in McStas that describe the neutron interaction with 70 different form and structure factors. We describe the considerations taken into account for the generation…
Oak Ridge National Laboratory (ORNL) experimental neutron science facilities produce 1.2\,TB a day of raw event-based data that is stored using the standard metadata-rich NeXus schema built on top of the HDF5 file format. Performance of…
The high performance requirements at the European Spallation Source have been driving the technological advances on the neutron detector front. Now more than ever is it important to optimize the design of detectors and instruments, to fully…
An open source software package for simulating thermal neutron propagation in geometry is presented. In this system, neutron propagation can be treated by either the particle transport method or the ray-tracing method. Supported by an…
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…
Machine learning has emerged as a powerful tool in materials discovery, enabling the rapid design of novel materials with tailored properties for countless applications, including in the context of energy and sustainability. To ensure the…
We present algorithmic improvements to the loading operations of certain reduced data ensembles produced from neutron scattering experiments at Oak Ridge National Laboratory (ORNL) facilities. Ensembles from multiple measurements are…
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…
The MAterials Simulation Toolkit (MAST) is a workflow manager and post-processing tool for ab initio defect and diffusion workflows. MAST codifies research knowledge and best practices for such workflows, and allows for the generation and…
Magnetic molecules, modelled as finite-size spin systems, are test-beds for quantum phenomena and could constitute key elements in future spintronics devices, long-lasting nanoscale memories or noise-resilient quantum computing platforms.…
NuSD: Neutrino Segmented Detector is a Geant4-based user application that simulates inverse beta decay event in a variety of segmented scintillation detectors developed by different international collaborations. This simulation framework…
During the last decades, neutron beam transportation has been a well-known and established subject for designing proper neutron guides. However, sometimes unusual adaptation or adjustments are required out of original projects and after…
Computational tools for normal mode analysis, which are widely used in physics and materials science problems, are designed here in a single package called NMscatt (Normal Modes & scattering) that allows arbitrarily large systems to be…
Multivariate time series anomaly detection (MTSAD) aims to accurately identify and localize complex abnormal patterns in the large-scale industrial control systems. While existing approaches excel in recognizing the distinct patterns under…
Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and…
An key element of the success of McStas is the component layer where users and developers alike are contributing to the description of new physical models and features. In McStas, components realise all physical elements of the simulated…
Combined Transmission and Distribution Systems (CoTDS) simulation for power systems requires development of algorithms and software that are numerically stable and at the same time accurately simulate dynamic events that can occur in…