No Arabic abstract
Novel multiplexing triple-axis neutron scattering spectrometers yield significant improvements of the common triple-axis instruments. While the planar scattering geometry keeps ensuring compatibility with complex sample environments, a simultaneous detection of scattered neutrons at various angles and energies leads to tremendous improvements in the data acquisition rate. Here we report on the software package MJOLNIR that we have developed to handle the resulting enhancement in data complexity. Using data from the new CAMEA spectrometer of the Swiss Spallation Neutron Source at the Paul Scherrer Institut, we show how the software reduces, visualises and treats observables measured on multiplexing spectrometers. The software package has been generalised to a uniformed framework, allowing for collaborations across multiplexing instruments at different facilities, further facilitating new developments in data treatment, such as fitting routines and modelling of multi-dimensional data.
A reliable and user-friendly characterisation of nano-objects in a target material is presented here in the form of a software data analysis package for interpreting small-angle X-ray scattering (SAXS) patterns. When provided with data on absolute scale with reasonable uncertainty estimates, the software outputs (size) distributions in absolute volume fractions complete with uncertainty estimates and minimum evidence limits, and outputs all distribution modes of a user definable range of one or more model parameters. A multitude of models are included, including prolate and oblate nanoparticles, core-shell objects, polymer models (Gaussian chain and Kholodenko worm) and a model for densely packed spheres (using the LMA-PY approximations). The McSAS software can furthermore be integrated as part of an automated reduction and analysis procedure in laboratory instruments or at synchrotron beamlines.
Neutron direct-geometry time-of-flight chopper spectroscopy is instrumental in studying fundamental excitations of vibrational and/or magnetic origin. We report here that techniques in super-resolution optical imagery (which is in real-space) can be adapted to enhance resolution and reduce noise for a neutron spectroscopy (an instrument for mapping excitations in reciprocal space). The procedure to reconstruct super-resolution energy spectra of phonon density of states relies on a realization of multi-frame registration, accurate determination of the energy-dependent point spread function, asymmetric nature of instrument resolution broadening, and iterative reconstructions. Applying these methods to phonon density of states data for a graphite sample demonstrates contrast enhancement, noise reduction, and ~5-fold improvement over nominal energy resolution. The data were collected at three different incident energies measured at the Wide Angular-Range Chopper Spectrometer at the Spallation Neutron Source.
Autonomous experiments are excellent tools to increase the efficiency of material discovery. Indeed, AI and ML methods can help optimizing valuable experimental resources as, for example, beam time in neutron scattering experiments, in addition to scientists knowledge and experience. Active learning methods form a particular class of techniques that acquire knowledge on a specific quantity of interest by autonomous decisions on what or where to investigate next based on previous measurements. For instance, Gaussian Process Regression (GPR) is a well-known technique that can be exploited to accomplish active learning tasks for scattering experiments as was recently demonstrated. Gaussian processes are not only capable to approximate functions by their posterior mean function, but can also quantify uncertainty about the approximation itself. Hence, if we perform function evaluations at locations of highest uncertainty, the function can be optimally learned in an iterative manner. We suggest the use of log-Gaussian processes, being a natural approach to successfully conduct autonomous neutron scattering experiments in general and TAS experiments with the instrument PANDA at MLZ in particular.
The Mantid framework is a software solution developed for the analysis and visualization of neutron scattering and muon spin measurements. The framework is jointly developed by software engineers and scientists at the ISIS Neutron and Muon Facility and the Oak Ridge National Laboratory. The objectives, functionality and novel design aspects of Mantid are described.
PyUnfold is a Python package for incorporating imperfections of the measurement process into a data analysis pipeline. In an ideal world, we would have access to the perfect detector: an apparatus that makes no error in measuring a desired quantity. However, in real life, detectors have finite resolutions, characteristic biases that cannot be eliminated, less than full detection efficiencies, and statistical and systematic uncertainties. By building a matrix that encodes a detectors smearing of the desired true quantity into the measured observable(s), a deconvolution can be performed that provides an estimate of the true variable. This deconvolution process is known as unfolding. The unfolding method implemented in PyUnfold accomplishes this deconvolution via an iterative procedure, providing results based on physical expectations of the desired quantity. Furthermore, tedious book-keeping for both statistical and systematic errors produces precise final uncertainty estimates.