Speaker
Description
Two-dimensional pixel detectors used at X-ray diffraction beamlines
provide planar cuts through the reciprocal-space representation of the
X-ray scattering function. With the advent of today’s high-brilliance
X-ray sources, the resulting very high signal-to-noise data demand
analysis pipelines that minimize both systematic errors and approximation
artifacts.
We present a new open-source software framework for converting
two-dimensional X-ray scattering images into normalized one- and
two-dimensional representations, $I(Q)$ and $I(Q,\phi)$. The framework
supports the combination of multiple detectors acquired simultaneously
and/or multiple images at different detector setting (e.g., at
different detector-sample distances).
While radial integration is straightforward if the sample scattered
symmetrically, any non-symmetric experiment, which due to polarization
and sample geometry is almost every experiment at these S/N ratio,
failure to account for the scattering symmetry operator when coupled
with an incomplete detector surface due to either masked pixels or
physical detector construction, results in bias data and artifacts.
This code provide a robust way to calculate the symmetry components for each
$Q$-bin and then correctly weight the pixel contributions in the
radial integrals to compensate for the missing non-symmetric pixel units
We demonstrate how this method improves data smoothness and scaling,
and significantly reduces Fourier artifacts in pair distribution
functions (PDFs) derived from such datasets. The software is designed
to be modular and easily embedded into existing data-analysis
environments, and is already integrated into real-time processing
workflows for ultrafast pump–probe experiments
[``Real-Time Data Sorting and Interaction at Ultrafast Pump-Probe
Experiments'' submitted by John Bekx].
