Experimental neutron spectroscopy data visualization: adaptive tessellation algorithm.
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.
Bustinduy
I
I
CSIC, Instituto de Estructura de la Materia, Serrano 123, Madrid, Spain.
Bermejo
F J
FJ
Perring
T G
TG
Bordel
G
G
D000465:Algorithms; D003461:Crystallography / Q000295:instrumentation / Q000379:methods; D007091:Image Processing, Computer-Assisted; D009502:Neutrons; D013057:Spectrum Analysis / Q000295:instrumentation / Q000379:methods