TY  - JOUR
AU  - Hartmann, Gregor
AU  - Goetzke, Gesa
AU  - Düsterer, Stefan
AU  - Feuer-Forson, Peter
AU  - Lever, Fabiano
AU  - Meier, David
AU  - Möller, Felix
AU  - Vera Ramirez, Luis
AU  - Guehr, Markus
AU  - Tiedtke, Kai
AU  - Viefhaus, Jens
AU  - Braune, Markus
TI  - Unsupervised real-world knowledge extraction via disentangled variational autoencoders for photon diagnostics
JO  - Scientific reports
VL  - 12
IS  - 1
SN  - 2045-2322
CY  - [London]
PB  - Macmillan Publishers Limited, part of Springer Nature
M1  - PUBDB-2022-07693
SP  - 20783
PY  - 2022
AB  - 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 instrumentfor online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-prioriknowledge the network is able to find representations of single-shot FEL spectra, which have a lowsignal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information aboutthe photon properties. The central 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 theremoval of artefacts. In the reconstruction, this allows for identification of signatures with very lowintensity which are hardly recognisable in the raw data. In this particular case, the network enhancesthe quality of the diagnostic analysis at FLASH. However, this unsupervised method also has thepotential to improve the analysis of other similar types of spectroscopy data.
LB  - PUB:(DE-HGF)16
C6  - pmid:36456706
UR  - <Go to ISI:>//WOS:000914086600079
DO  - DOI:10.1038/s41598-022-25249-4
UR  - https://bib-pubdb1.desy.de/record/490376
ER  -