001     622842
005     20250804160532.0
024 7 _ |a 10.1038/s42005-024-01900-6
|2 doi
024 7 _ |a 10.3204/PUBDB-2025-00522
|2 datacite_doi
024 7 _ |a altmetric:172183126
|2 altmetric
024 7 _ |a WOS:001380084800001
|2 WOS
037 _ _ |a PUBDB-2025-00522
041 _ _ |a English
082 _ _ |a 530
100 1 _ |a Ferreira de Lima, Danilo Enoque
|0 P:(DE-H253)PIP1028636
|b 0
|e Corresponding author
245 _ _ |a Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser
260 _ _ |a London
|c 2024
|b Springer Nature
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1738577565_2940589
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a A reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. In this paper, we present a method based on machine learning, the virtual spectrometer, that improves the resolution of non-invasive spectral diagnostics at the European XFEL by up to 40%, and significantly increases its signal-to-noise ratio. This improves the reliability of quasi-real-time monitoring, which is critical to steer the experiment, as well as the interpretation of experimental outcomes. Furthermore, the virtual spectrometer streamlines and automates the calibration of the spectral diagnostic device, which is otherwise a complex and time-consuming task, by virtue of its underlying detection principles. Additionally, the provision of robust quality metrics and uncertainties enable a transparent and reliable validation of the tool during its operation. A complete characterization of the virtual spectrometer under a diverse set of experimental and simulated conditions is provided in the manuscript, detailing advantages and limits, as well as its robustness with respect to the different test cases.
536 _ _ |a 6G13 - Accelerator of European XFEL (POF4-6G13)
|0 G:(DE-HGF)POF4-6G13
|c POF4-6G13
|f POF IV
|x 0
536 _ _ |a DIGIPREDICT - Edge AI-deployed DIGItal Twins for PREDICTing disease progression and need for early intervention in infectious and cardiovascular diseases beyond COVID-19 (101017915)
|0 G:(EU-Grant)101017915
|c 101017915
|f H2020-FETPROACT-2020-2
|x 1
536 _ _ |a NETCO-PD - NETCO-PD: 14 experienced researchers in network science for Europe (101034253)
|0 G:(EU-Grant)101034253
|c 101034253
|f H2020-MSCA-COFUND-2020
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: bib-pubdb1.desy.de
693 _ _ |a XFEL
|e Experiments at XFEL
|1 EXP:(DE-H253)XFEL-20150101
|0 EXP:(DE-H253)XFEL-Exp-20150101
|5 EXP:(DE-H253)XFEL-Exp-20150101
|x 0
700 1 _ |a Davtyan, Arman
|b 1
700 1 _ |a Laksman, Joakim
|b 2
700 1 _ |a Gerasimova, Natalia
|b 3
700 1 _ |a Maltezopoulos, Theophilos
|b 4
700 1 _ |a Liu, Jia
|0 P:(DE-H253)PIP1019426
|b 5
700 1 _ |a Schmidt, Philipp
|b 6
700 1 _ |a Michelat, Thomas
|b 7
700 1 _ |a Mazza, Tommaso
|b 8
700 1 _ |a Meyer, Michael
|b 9
700 1 _ |a Grünert, Jan
|b 10
700 1 _ |a Gelisio, Luca
|b 11
773 _ _ |a 10.1038/s42005-024-01900-6
|g Vol. 7, no. 1, p. 400
|0 PERI:(DE-600)2921913-9
|n 1
|p 400
|t Communications Physics
|v 7
|y 2024
|x 2399-3650
856 4 _ |y OpenAccess
|u https://bib-pubdb1.desy.de/record/622842/files/s42005-024-01900-6.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://bib-pubdb1.desy.de/record/622842/files/s42005-024-01900-6.pdf?subformat=pdfa
909 C O |o oai:bib-pubdb1.desy.de:622842
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p ec_fundedresources
|p openCost
|p dnbdelivery
910 1 _ |a European XFEL
|0 I:(DE-588)1043621512
|k XFEL.EU
|b 0
|6 P:(DE-H253)PIP1028636
910 1 _ |a European XFEL
|0 I:(DE-588)1043621512
|k XFEL.EU
|b 5
|6 P:(DE-H253)PIP1019426
913 1 _ |a DE-HGF
|b Forschungsbereich Materie
|l Großgeräte: Materie
|1 G:(DE-HGF)POF4-6G0
|0 G:(DE-HGF)POF4-6G13
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-600
|4 G:(DE-HGF)POF
|v Accelerator of European XFEL
|x 0
914 1 _ |y 2024
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-20
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-20
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2024-12-20
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b COMMUN PHYS-UK : 2022
|d 2024-12-20
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b COMMUN PHYS-UK : 2022
|d 2024-12-20
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-04-10T15:36:49Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-04-10T15:36:49Z
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-20
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2024-12-20
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-20
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2024-12-20
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2024-12-20
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2024-12-20
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-20
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-20
915 p c |a APC keys set
|2 APC
|0 PC:(DE-HGF)0000
915 p c |a Local Funding
|2 APC
|0 PC:(DE-HGF)0001
915 p c |a DFG OA Publikationskosten
|2 APC
|0 PC:(DE-HGF)0002
915 p c |a DOAJ Journal
|2 APC
|0 PC:(DE-HGF)0003
915 p c |a DEAL: Springer Nature 2020
|2 APC
|0 PC:(DE-HGF)0113
920 1 _ |0 I:(DE-H253)XFEL_DO_DD_DA-20210408
|k XFEL_DO_DD_DA
|l Data Analysis
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-H253)XFEL_DO_DD_DA-20210408
980 _ _ |a APC
980 1 _ |a APC
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21