Naive data mining and machine learning for high resolution, sparse x-ray spectra

Due to recent advancements in low-temperature detectors for x-ray emission spectroscopy (XES), we are now able to generate higher resolution spectra with larger bandwidth than previously attainable. While this more accurate detection technology will allow for more reliable sample classification, current analysis is insufficient to handle the new level of detail. The limitations of current classification methods is exacerbated by the difficulty producing unifying theory for the physical mechanisms responsible for x-ray emission. Currently, it is well understood how to detect elemental concentrations in a sample, but not the chemical composition. To address this gap in the data analysis, we use a domain-knowledge naive approach to data mining to discover regions-of-interest (ROIs) relevant to oxidation state of chemical compounds. Typical features of the spectra in the found ROIs, such as relative peak heights and location, are used in highly transparent machine learning methods. We also train a convolutional neural network (CNN) using Monte-Carlo derived data based on a recorded training set of spectra. Both methods are applied to a testing set of the same compounds measures a year prior. Our results indicate that, while the ROIs found using the naive mining approach potentially carry information regarding oxidation state, the signatures are more subtle than relative peak heights and positions. This is supported by saliency maps of the CNN when classifying the test set. These methods are the first step to gaining the understanding of XES required to use this detection technology in a real-time chemical imaging application.