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Experimental neutron spectroscopy data visualization: adaptive tessellation algorithm.

Bustinduy I, Bermejo FJ, Perring TG, Bordel G

CSIC, Instituto de Estructura de la Materia, Serrano 123, Madrid, Spain.

We report on an adaptive binning approach designed for data visualization within scientific disciplines where counting statistics are expected to follow Poisson distributions. We envisage a wide range of applications stemming from astrophysics to the condensed matter sciences. Our main focus of interest concerns, however, neutron spectroscopy data from single-crystal samples where signals span a four-dimensional space defined by three spatial coordinates plus time. This makes widely used equal-width binning schemes inadequate since physically relevant information is often concentrated within rather small regions of such a space. Our aim is thus to generate optimally binned data sets from one-dimensional to three-dimensional volumes to provide the experimentalist with enhanced ability to carry out searches within a four-dimensional space. Several binning algorithms are then scrutinized against experimental as well as simulated data.

Published 4 May 2007 in Rev Sci Instrum, 78(4): 043901.
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