Emergence and distinction of classes in XRD data via machine learning

The material-specific information contained in X-ray diffraction (XRD) measurements make it attractive for the detection of threats in airport baggage. Spatially-localized XRD signatures at each voxel in a bag may be obtained with a snapshot via coded aperture XRD tomography, but measurement unceratinty due to data processing and low SNR can lead to loss in information. We use machine learning and non-linear dimension reduction to identify threat and non-threat items in a way that overcomes these variations in the data. We observe the emergence of clusters from the data, possibly providing new prospects for XRD-based classification. We further show improved performance using machine learning methods relative to a conventional, correlation-based classifier in the low-SNR regime.