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000582754 005__ 20231101210623.0
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000582754 0247_ $$2datacite_doi$$a10.3204/PUBDB-2023-02144
000582754 037__ $$aPUBDB-2023-02144
000582754 041__ $$aEnglish
000582754 1001_ $$0P:(DE-H253)PIP1095777$$aGrech, Christian$$b0$$eCorresponding author
000582754 1112_ $$a14th International Particle Accelerator Conference$$cVenice$$d2023-05-07 - 2023-05-12$$gIPAC2023$$wItaly
000582754 245__ $$aVirtual Photon Pulse Characterisation using Machine Learning methods
000582754 260__ $$a[Geneva]$$bJACoW Publishing$$c2023
000582754 29510 $$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],
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000582754 520__ $$aThe use of fast computational tools is important in the operation of X-ray free electron lasers, in order to predict the output of diagnostics when they are either destructive or unavailable. Physics-based simulations can be computationally intensive to provide estimates on a real-time basis. This proposed work explores the use of machine learning to provide operators with estimates of key photon pulse characteristics related to beam pointing. A data pipeline is set up and the method is applied to the SASE1 undulator line at the European XFEL. This case study evaluates the performance of the model for different amounts of training data.
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000582754 650_7 $$2Other$$aAccelerator Physics
000582754 650_7 $$2Other$$amc6-beam-instrumentation-controls-feedback-and-operational-aspects - MC6: Beam Instrumentation, Controls, Feedback and Operational Aspects
000582754 650_7 $$2Other$$amc6-a27-machine-learning-and-digital-twin-modelling - MC6.A27: Machine Learning and Digital Twin Modelling
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000582754 7001_ $$0P:(DE-H253)PIP1080379$$aJafarinia, Farzad$$b1
000582754 7001_ $$0P:(DE-H253)PIP1080263$$aGuetg, Marc$$b2
000582754 7001_ $$0P:(DE-H253)PIP1000427$$aGeloni, Gianluca$$b3
000582754 7001_ $$0P:(DE-H253)PIP1093124$$aGuest, Trey$$b4
000582754 773__ $$a10.18429/JACoW-IPAC2023-THPL020
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