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We report on a new algorithm for detection of crystallographic information in3D, as retained in Atom Probe Tomography (APT), with improved robustness andsignal detection performance. The algorithm is underpinned by 1D distributionfunctions, as per existing algorithms, but eliminates an unnecessary parameteras compared to current methods. By examining traditional distribution functionsin an automated fashion in real space, rather than using Fourier transformapproaches, we utilise an error metric based upon the expected value for aspatially random distribution for detecting crystallography. We show caseswhere the metric is able to successfully obtain orientation information, andshow that it can function with high levels of additive and displacivebackground noise. We additionally compare this metric to Fourier transformmethods, showing fewer artefacts when examining simulated datasets. Anextension of the approach is used to aid the automatic detection ofhigh-quality data regions within an entire dataset, albeit with a largeincrease in computational cost. This extension is demonstrated on acquiredAluminium and Tungsten APT datasets, and shown to be able to discern regions ofthe data which have relatively improved spatial data quality. Finally, thisprogram has been made available for use in other laboratories undertaking theirown analyses.

DF-Fit : A robust algorithm for detection of crystallographic information in Atom Probe Tomography data