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RIS citation export for MOPOTK013: Machine Learning Based Surrogate Model Construction for Optics Matching at the European XFEL

TY  - CONF
AU  - Zhu, Z.H.
AU  - Chen, Y.
AU  - Qin, W.
AU  - Scholz, M.
AU  - Tomin, S.
ED  - Zimmermann, Frank
ED  - Tanaka, Hitoshi
ED  - Sudmuang, Porntip
ED  - Klysubun, Prapong
ED  - Sunwong, Prapaiwan
ED  - Chanwattana, Thakonwat
ED  - Petit-Jean-Genaz, Christine
ED  - Schaa, Volker R.W.
TI  - Machine Learning Based Surrogate Model Construction for Optics Matching at the European XFEL
J2  - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022
CY  - Bangkok, Thailand
T2  - International Particle Accelerator Conference
T3  - 13
LA  - english
AB  - Beam optics matching is a daily routine in the operation of an X-ray free-electron laser facility. Usually, linear optics is employed to conduct the beam matching in the control room. However, the collective effects like space charge dominate the electron bunch in the low-energy region which decreases the accuracy of the existing tool. Therefore, we proposed a scheme to construct a surrogate model with nonlinear optics and collective effects to speed up the optics matching in the European XFEL injector section. This model also facilitates further research on beam dynamics for the space-charge dominated beam.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 461
EP  - 464
KW  - optics
KW  - simulation
KW  - quadrupole
KW  - electron
KW  - FEL
DA  - 2022/07
PY  - 2022
SN  - 2673-5490
SN  - 978-3-95450-227-1
DO  - doi:10.18429/JACoW-IPAC2022-MOPOTK013
UR  - https://jacow.org/ipac2022/papers/mopotk013.pdf
ER  -