TY  - CONF
AU  - Grech, Christian
AU  - Jafarinia, Farzad
AU  - Guetg, Marc
AU  - Geloni, Gianluca
AU  - Guest, Trey
TI  - Virtual Photon Pulse Characterisation using Machine Learning methods
CY  - [Geneva]
PB  - JACoW Publishing
M1  - PUBDB-2023-02144
SN  - 978-3-95450-231-8
SP  - 4468-4470
PY  - 2023
N1  - Literaturangaben;
AB  - 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.
T2  - 14th International Particle Accelerator Conference
CY  - 7 May 2023 - 12 May 2023, Venice (Italy)
Y2  - 7 May 2023 - 12 May 2023
M2  - Venice, Italy
KW  - Accelerator Physics (Other)
KW  - mc6-beam-instrumentation-controls-feedback-and-operational-aspects - MC6: Beam Instrumentation, Controls, Feedback and Operational Aspects (Other)
KW  - mc6-a27-machine-learning-and-digital-twin-modelling - MC6.A27: Machine Learning and Digital Twin Modelling (Other)
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.18429/JACoW-IPAC2023-THPL020
UR  - https://bib-pubdb1.desy.de/record/582754
ER  -