Abstract
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.
Highlights
Recent progress in materials science using computational approaches such as machine learning and related methods is remarkable[1]
Researchers determine measurement time of SAS experiments empirically to get statistically reliable data, which is often based on a visual impression of data or a rule of thumb taught by their supervisors or instrument scientists
This paper aims to show that long-ignored spatial correlation of SAS data can give us statistical benefit leading to more efficient use of beamtime at large facilities
Summary
Recent progress in materials science using computational approaches such as machine learning and related methods is remarkable[1]. Filling blank entries in a database with experimental results often requires substantial efforts in sample synthesis, measurement, and analysis Making these processes more efficient is a key to data-driven materials science. Synchrotron SAXS and SANS are only available at a limited number of facilities around the world, and, as a result, getting beamtime for SAS experiments at those facilities is so competitive that one can only perform a few experiments per year In such situation, any attempts to make SAS measurements more efficient are of significance for advances in microstructural studies, especially regarding data-driven materials science. Researchers determine measurement time of SAS experiments (or any types of counting experiments, perhaps) empirically to get statistically reliable data, which is often based on a visual impression of data or a rule of thumb taught by their supervisors or instrument scientists While this practice has supposedly worked so far to assure data quality, it does not mean that there is no need for improvement. The idea we propose here is so simple that most of the experiments using a two-dimensional detector can benefit from it with appropriate modification and consideration for each use case
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
