Accelerating small-angle scattering experiments on anisotropic samples using kernel density estimation

摘要

We propose a method to accelerate small-angle scattering experiments by exploiting spatial correlation in two-dimensional data. We applied kernel density estimation to the average of a hundred short scans and evaluated noise reduction effects of kernel density estimation (smoothing). Although there is no advantage of using smoothing for isotropic data due to the powerful noise reduction effect of radial averaging, smoothing with a statistically and physically appropriate kernel can shorten measurement time by less than half to obtain sector averages with comparable statistical quality to that of sector averages without smoothing. This benefit will encourage researchers not to use full radial average on anisotropic data sacrificing anisotropy for statistical quality. We also confirmed that statistically reasonable estimation of measurement time is feasible on site by evaluating how intensity variances improve with accumulating counts. The noise reduction effect of smoothing will bring benefits to a wide range of applications from efficient use of beamtime at laboratories and large experimental facilities to stroboscopic measurements suffering low statistical quality.