ArticleScientific reports2022
Unsupervised real-world knowledge extraction via disentangled variational autoencoders for photon diagnostics.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators.Scientific reports · 2024Article
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Authors and funding
12 authors.
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Abstract
We present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument for online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-priori knowledge the network is able to find representations of single-shot FEL spectra, which have a low signal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information about the photon properties. The central photon energy and the intensity as well as very detector-specific features are identified. The network is also capable of data cleaning, i.e. denoising, as well as the removal of artefacts. In the reconstruction, this allows for identification of signatures with very low intensity which are hardly recognisable in the raw data. In this particular case, the network enhances the quality of the diagnostic analysis at FLASH. However, this unsupervised method also has the potential to improve the analysis of other similar types of spectroscopy data.
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