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

TL;DR

This paper demonstrates how disentangled variational autoencoders can analyze noisy photon spectra from free electron laser diagnostics, revealing key photon properties and improving data quality without prior knowledge.

Contribution

It introduces an unsupervised neural network approach using disentangled variational autoencoders for real-world photon spectrum analysis at FELs, enabling interpretation and denoising.

Findings

Successfully identified photon energy, intensity, and detector-specific features.

Enhanced data quality and artifact removal in FEL spectra.

Potential applicability to other spectroscopy data analysis.

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.