We report on a new algorithm for detection of crystallographic information in
3D, as retained in Atom Probe Tomography (APT), with improved robustness and
signal detection performance. The algorithm is underpinned by 1D distribution
functions, as per existing algorithms, but eliminates an unnecessary parameter
as compared to current methods. By examining traditional distribution functions
in an automated fashion in real space, rather than using Fourier transform
approaches, we utilise an error metric based upon the expected value for a
spatially random distribution for detecting crystallography. We show cases
where the metric is able to successfully obtain orientation information, and
show that it can function with high levels of additive and displacive
background noise. We additionally compare this metric to Fourier transform
methods, showing fewer artefacts when examining simulated datasets. An
extension of the approach is used to aid the automatic detection of
high-quality data regions within an entire dataset, albeit with a large
increase in computational cost. This extension is demonstrated on acquired
Aluminium and Tungsten APT datasets, and shown to be able to discern regions of
the data which have relatively improved spatial data quality. Finally, this
program has been made available for use in other laboratories undertaking their
own analyses