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PUBLICATIONS

Poisson errors and adaptive rebinning in X-ray Powder Diffraction Data

Published

Author(s)

Marcus H. Mendenhall

Abstract

Abstract This work provides a short summary of techniques for formally-correct handling of statistical uncertainties in Poisson- statistics dominated data. Correct assignment of uncertainties for low counts is documented. We describe a technique for adaptively rebinning such data sets to provide more uniform statistics across a pattern with a wide range of count rates, from a few (or no) counts in a background bin to on-peak regions with many counts. This permits better plotting of data and analysis of a smaller number of points in a fitting package, without significant degradation of the information content of the data set. Examples of the effect of this on a data set are given.
Citation
Powder Diffraction
Volume
33
Issue
4
Pub Type
Journals

Download Paper

https://doi.org/10.1017/S0885715618000726

Keywords

statistics, x-ray diffraction, xrd, poisson, data analysis, adaptive
Image and signal processing, Numerical methods and software, Statistical analysis and Uncertainty quantification

Citation

Mendenhall, M. (2018), Poisson errors and adaptive rebinning in X-ray Powder Diffraction Data, Powder Diffraction, [online], https://doi.org/10.1017/S0885715618000726 (Accessed May 21, 2025)

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Created October 10, 2018, Updated December 20, 2018