Spectrometer Optimization and Alignment

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The aim of this project is to utilise machine learning methods for the optimisation of the multi-parameter design space of a soft x-ray fluorescence spectrometer and to assist in the efficient relative alignment of it's components. The alignment of the reflection zone plate relative to the experiment sample is currently a time consuming manual process. By training a neural network to recognize the output of the camera in relation to XYZ-coordinates, real-world offsets can be ascertained and an efficient alignment is possible.