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| 001 | 582754 | ||
| 005 | 20231101210623.0 | ||
| 020 | _ | _ | |a 978-3-95450-231-8 |
| 024 | 7 | _ | |a 10.18429/JACoW-IPAC2023-THPL020 |2 doi |
| 024 | 7 | _ | |a 10.3204/PUBDB-2023-02144 |2 datacite_doi |
| 037 | _ | _ | |a PUBDB-2023-02144 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Grech, Christian |0 P:(DE-H253)PIP1095777 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 14th International Particle Accelerator Conference |g IPAC2023 |c Venice |d 2023-05-07 - 2023-05-12 |w Italy |
| 245 | _ | _ | |a Virtual Photon Pulse Characterisation using Machine Learning methods |
| 260 | _ | _ | |a [Geneva] |c 2023 |b JACoW Publishing |
| 295 | 1 | 0 | |a [Ebook] IPAC'23 : 14th International Particle Accelerator Conference, 7-12 May 2023, Venice, Italy : proceedings / hosting institutions: INFN, Elettra Sincotrone Trieste , [Geneva] : JACoW Publishing, [2023], |
| 300 | _ | _ | |a 4468-4470 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1698844062_1121339 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 500 | _ | _ | |a Literaturangaben; |
| 520 | _ | _ | |a The use of fast computational tools is important in the operation of X-ray free electron lasers, in order to predict the output of diagnostics when they are either destructive or unavailable. Physics-based simulations can be computationally intensive to provide estimates on a real-time basis. This proposed work explores the use of machine learning to provide operators with estimates of key photon pulse characteristics related to beam pointing. A data pipeline is set up and the method is applied to the SASE1 undulator line at the European XFEL. This case study evaluates the performance of the model for different amounts of training data. |
| 536 | _ | _ | |a 621 - Accelerator Research and Development (POF4-621) |0 G:(DE-HGF)POF4-621 |c POF4-621 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Accelerator Physics |2 Other |
| 650 | _ | 7 | |a mc6-beam-instrumentation-controls-feedback-and-operational-aspects - MC6: Beam Instrumentation, Controls, Feedback and Operational Aspects |2 Other |
| 650 | _ | 7 | |a mc6-a27-machine-learning-and-digital-twin-modelling - MC6.A27: Machine Learning and Digital Twin Modelling |2 Other |
| 693 | _ | _ | |a XFEL |e Facility (machine) XFEL |1 EXP:(DE-H253)XFEL-20150101 |0 EXP:(DE-H253)XFEL(machine)-20150101 |5 EXP:(DE-H253)XFEL(machine)-20150101 |x 0 |
| 700 | 1 | _ | |a Jafarinia, Farzad |0 P:(DE-H253)PIP1080379 |b 1 |
| 700 | 1 | _ | |a Guetg, Marc |0 P:(DE-H253)PIP1080263 |b 2 |
| 700 | 1 | _ | |a Geloni, Gianluca |0 P:(DE-H253)PIP1000427 |b 3 |
| 700 | 1 | _ | |a Guest, Trey |0 P:(DE-H253)PIP1093124 |b 4 |
| 773 | _ | _ | |a 10.18429/JACoW-IPAC2023-THPL020 |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/582754/files/HTML-Approval_of_scientific_publication.html |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/582754/files/PDF-Approval_of_scientific_publication.pdf |
| 856 | 4 | _ | |y OpenAccess |u https://bib-pubdb1.desy.de/record/582754/files/THPL020.pdf |
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| 910 | 1 | _ | |a Deutsches Elektronen-Synchrotron |0 I:(DE-588b)2008985-5 |k DESY |b 0 |6 P:(DE-H253)PIP1095777 |
| 910 | 1 | _ | |a Deutsches Elektronen-Synchrotron |0 I:(DE-588b)2008985-5 |k DESY |b 1 |6 P:(DE-H253)PIP1080379 |
| 910 | 1 | _ | |a Deutsches Elektronen-Synchrotron |0 I:(DE-588b)2008985-5 |k DESY |b 2 |6 P:(DE-H253)PIP1080263 |
| 910 | 1 | _ | |a European XFEL |0 I:(DE-588)1043621512 |k XFEL.EU |b 2 |6 P:(DE-H253)PIP1080263 |
| 910 | 1 | _ | |a European XFEL |0 I:(DE-588)1043621512 |k XFEL.EU |b 3 |6 P:(DE-H253)PIP1000427 |
| 910 | 1 | _ | |a European XFEL |0 I:(DE-588)1043621512 |k XFEL.EU |b 4 |6 P:(DE-H253)PIP1093124 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Materie |l Materie und Technologie |1 G:(DE-HGF)POF4-620 |0 G:(DE-HGF)POF4-621 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-600 |4 G:(DE-HGF)POF |v Accelerator Research and Development |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 920 | 1 | _ | |0 I:(DE-H253)MXL-20160301 |k MXL |l Koordination des XFEL-Beschleunigers |x 0 |
| 980 | _ | _ | |a contrib |
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| 980 | _ | _ | |a UNRESTRICTED |
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| 980 | _ | _ | |a I:(DE-H253)MXL-20160301 |
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