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

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

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-pri...

Full description

Bibliographic Details
Main Authors: Gregor Hartmann, Gesa Goetzke, Stefan Düsterer, Peter Feuer-Forson, Fabiano Lever, David Meier, Felix Möller, Luis Vera Ramirez, Markus Guehr, Kai Tiedtke, Jens Viefhaus, Markus Braune
Format: Article
Language:English
Published: Nature Portfolio 2022-12-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-25249-4

Internet

https://doi.org/10.1038/s41598-022-25249-4

Similar Items