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RIS citation export for THYGBE1: Applying Artificial Intelligence to Accelerators

TY - CONF
AU - Scheinker, A.
AU - Bohler, D.K.
AU - Edelen, A.L.
AU - Garnett, R.W.
AU - Milton, S.V.
AU - Rees, D.
ED - Koscielniak, Shane
ED - Satogata, Todd
ED - Schaa, Volker RW
ED - Thomson, Jana
TI - Applying Artificial Intelligence to Accelerators
J2 - Proc. of IPAC2018, Vancouver, BC, Canada, April 29-May 4, 2018
C1 - Vancouver, BC, Canada
T2 - International Particle Accelerator Conference
T3 - 9
LA - english
AB - Particle accelerators are being designed and operated over a wide range of complex beam phase space distributions. For example, the Linac Coherent Light Source (LCLS) upgrade, LCLS-II, is considering complex schemes such as two-color operation [1], while the plasma wake field acceleration facility for advanced accelerator experimental tests (FACET) upgrade, FACET-II, is planning on providing custom tailored current profiles [2]. Because of uncertainty due to limited diagnostics and time varying performance, such as thermal drifts, as well as collective effects and the complex coupling of large numbers of components, it is impossible to use simple look up tables for parameter settings in order to quickly switch between widely varying operating ranges. Several forms of artificial intelligence are currently being investigated in order to enable accelerators to quickly and automatically re-adjust component settings without human intervention. In this work we discuss recent progress in applying neural networks and adaptive feedback algorithms to enable automatic accelerator tuning and optimization.
PB - JACoW Publishing
CP - Geneva, Switzerland
SP - 2925
EP - 2928
KW - FEL
KW - controls
KW - feedback
KW - electron
KW - network
DA - 2018/06
PY - 2018
SN - 978-3-95450-184-7
DO - 10.18429/JACoW-IPAC2018-THYGBE1
UR - http://jacow.org/ipac2018/papers/thygbe1.pdf
ER -