Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses

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dc.date.accessioned2023-04-28T14:38:02Z
dc.date.available2023-04-28T14:38:02Z
dc.date.issued2022-10-24
dc.description.sponsorshipGefördert durch den Publikationsfonds der Universität Kasselger
dc.identifierdoi:10.17170/kobra-202304287915
dc.identifier.urihttp://hdl.handle.net/123456789/14640
dc.language.isoeng
dc.relation.doidoi:10.1038/s41598-022-21646-x
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectApplied physicseng
dc.subjectCharacterization and analytical techniqueseng
dc.subjectComputer scienceeng
dc.subjectStatisticseng
dc.subject.ddc530
dc.subject.swdAngewandte Physikger
dc.subject.swdRöntgenlaserger
dc.subject.swdUltrakurzzeitlaserger
dc.subject.swdKünstliche Intelligenzger
dc.titleArtificial intelligence for online characterization of ultrashort X-ray free-electron laser pulseseng
dc.typeAufsatz
dc.type.versionpublishedVersion
dcterms.abstractX-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.eng
dcterms.accessRightsopen access
dcterms.creatorDingel, Kristina
dcterms.creatorOtto, Thorsten
dcterms.creatorMarder, Lutz
dcterms.creatorFunke, Lars
dcterms.creatorHeld, Arne
dcterms.creatorSavio, Sara
dcterms.creatorHans, Andreas
dcterms.creatorHartmann, Gregor
dcterms.creatorMeier, David
dcterms.creatorViefhaus, Jens
dcterms.creatorSick, Bernhard
dcterms.creatorEhresmann, Arno
dcterms.creatorIlchen, Markus
dcterms.creatorHelml, Wolfram
dcterms.extent14 Seiten
dcterms.source.articlenumber17809
dcterms.source.identifiereissn:2045-2322
dcterms.source.journalScientific Reportseng
dcterms.source.volumeVolume 12
kup.iskupfalse

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