Wavelength frame multiplication for reflectometry at long-pulse neutron sources

The European Spallation Source (ESS), which is under construction in Lund (Sweden), will be the next leading neutron facility with an unprecedented brilliance and novel long-pulse time structure. A long-pulse source not only provides a high time-average flux but also opens the possibility to tune the resolution by using pulse shaping choppers. Thus, an instrument can readily be operated in either a high flux or a high resolution mode. Several of the shorter instruments at the ESS will employ Wavelength Frame Multiplication (WFM) in order to enable a sufficient resolution while offering a continuous and broad wavelength range. A test beamline was operated until the end of 2019 at the research reactor in Berlin to test components and methods, including WFM, in order to prepare the new facility for the operation of neutron instruments and successful first science. We herein demonstrate the implementation of WFM for reflectometry. By selecting a short pulse mode under the same geometrical configuration, we compare and discuss the results for two reference samples. The reported experiments not only serve to prove the reliability of the WFM approach but also, for the first time, demonstrate the full instrument control, data acquisition and data reduction chain that will be implemented at the ESS.

Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python

scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implicit propagation of uncertainties, (iii) support for histograms, i.e., bin-edge coordinate axes, which exceed the data’s dimension extent by one, and (iv) support for event data. In conjunction these features enable a more natural and more concise user experience. The combination of named dimensions, coordinates, and units helps to drastically reduce the risk for programming errors. The core of scipp is written in C++ to open opportunities for performance improvements that a Python-based solution would not allow for. On top of the C++ core, scipp’s Python components provide functionality for plotting and content representations, e.g., for use in Jupyter Notebooks. While none of scipp’s concepts in isolation is novel per-se, we are not aware of any project combining all of these aspects in a single coherent software package.