Investment in brighter sources and larger detectors has resulted in an explosive rise in the data collected at synchrotron facilities. Currently, human experts extract scientific information from these data, but they cannot keep pace with the rate of data collection. Here, we present three on-the-fly approaches - attribute extraction, nearest-neighbor distance, and cluster analysis - to quickly segment x-ray diffraction (XRD) data into groups with similar XRD profiles. An expert can then analyze representative spectra from each group in detail with much reduced time, but without loss of scientific insights. On-the-fly segmentation would, therefore, result in accelerated scientific productivity.
@article{arxiv.1709.09225,
title = {On-the-fly Segmentation Approaches for X-ray Diffraction Datasets for Metallic Glasses},
author = {Fang Ren and Travis Williams and Jason Hattrick-Simpers and Apurva Mehta},
journal= {arXiv preprint arXiv:1709.09225},
year = {2017}
}