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Register a new userMcSAS: A package for extracting quantitative form-free distributions
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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.
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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.
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.
The distribution of photoelectrons acquired in angle-resolved photoemission spectroscopy can be mapped onto energy-momentum space of the Bloch electrons in the crystal. The explicit forms of the mapping function $f$ depend on the configuration of the apparatus as well as on the type of the photoelectron analyzer. We show that the existence of the analytic forms of $f^{text{-}1}$ is guaranteed in a variety of setups. The variety includes the case when the analyzer is equipped with a photoelectron deflector. Thereby, we provide a demonstrative mapping program implemented by an algorithm that utilizes both $f$ and $f^{text{-}1}$. The mapping methodology is also usable in other spectroscopic methods such as momentum-resolved electron-energy loss spectroscopy.
Modern analysis of high energy physics (HEP) data needs advanced statistical tools to separate signal from background. A C++ package has been implemented to provide such tools for the HEP community. The package includes linear and quadratic discriminant analysis, decision trees, bump hunting (PRIM), boosting (AdaBoost), bagging and random forest algorithms, and interfaces to the standard backpropagation neural net and radial basis function neural net implemented in the Stuttgart Neural Network Simulator. Supplemental tools such as bootstrap, estimation of data moments, and a test of zero correlation between two variables with a joint elliptical distribution are also provided. The package offers a convenient set of tools for imposing requirements on input data and displaying output. Integrated in the BaBar computing environment, the package maintains a minimal set of external dependencies and therefore can be easily adapted to any other environment. It has been tested on many idealistic and realistic examples.
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.
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