Contribution to a conference proceedings/Contribution to a book

PUBDB-2023-02144

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Virtual Photon Pulse Characterisation using Machine Learning methods

Grech, C. (Corresponding author)DESY* ; Jafarinia, F.DESY* ; Guetg, M.XFEL.EU*DESY* ; Geloni, G.XFEL.EU* ; Guest, T.XFEL.EU*

2023
JACoW Publishing [Geneva]
ISBN: 978-3-95450-231-8

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Please use a persistent id in citations: doi:10.18429/JACoW-IPAC2023-THPL020  doi:10.3204/PUBDB-2023-02144

Abstract: 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.

Keyword(s): Accelerator Physics ; mc6-beam-instrumentation-controls-feedback-and-operational-aspects - MC6: Beam Instrumentation, Controls, Feedback and Operational Aspects ; mc6-a27-machine-learning-and-digital-twin-modelling - MC6.A27: Machine Learning and Digital Twin Modelling

Note: Literaturangaben;

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Contributing Institute(s):

1. Koordination des XFEL-Beschleunigers (MXL)

Research Program(s):

1. 621 - Accelerator Research and Development (POF4-621) (POF4-621)

Experiment(s):

1. Facility (machine) XFEL

Appears in the scientific report 2023

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