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Poisson errors and adaptive rebinning in X-ray powder diffraction data

Published online by Cambridge University Press:  10 October 2018

Marcus H. Mendenhall Open the ORCID record for Marcus H. Mendenhall [Opens in a new window]
Marcus H. Mendenhall*
Affiliation:
National Institute of Standards and Technology (NIST), 100 Bureau Drive, Gaithersburg, MD 20899, USA
*
a)Author to whom correspondence should be addressed. Electronic mail: marcus.mendenhall@nist.gov
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Abstract

This work provides a short summary of techniques for formally-correct handling of statistical uncertainties in Poisson-statistics dominated data, with emphasis on X-ray powder diffraction patterns. Correct assignment of uncertainties for low counts is documented. Further, 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 diffraction data set are given.

Keywords

powder diffractionpoisson statisticsadaptive rebinning

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Type
Technical Article
Information
Powder Diffraction , Volume 33 , Issue 4 , December 2018 , pp. 266 - 269
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Creative Commons
This is a work of the U.S. Government and is not subject to copyright protection in the United States
Copyright
Copyright © International Centre for Diffraction Data 2018

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