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We present real-world data processing on measured electron time-of-flightdata via neural networks. Specifically, the use of disentangled variationalautoencoders on data from a diagnostic instrument for online wavelengthmonitoring at the free electron laser FLASH in Hamburg. Without a-prioriknowledge the network is able to find representations of single-shot FELspectra, which have a low signal-to-noise ratio. This reveals, in a directlyhuman-interpretable way, crucial information about the photon properties. Thecentral photon energy and the intensity as well as very detector-specificfeatures are identified. The network is also capable of data cleaning, i.e.denoising, as well as the removal of artefacts. In the reconstruction, thisallows for identification of signatures with very low intensity which arehardly recognisable in the raw data. In this particular case, the networkenhances the quality of the diagnostic analysis at FLASH. However, thisunsupervised method also has the potential to improve the analysis of othersimilar types of spectroscopy data.

Unsupervised real-world knowledge extraction via disentangled variational autoencoders for photon diagnostics