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RIS citation export for THPOTK061: Machine Learning Approach to Temporal Pulse Shaping for the Photoinjector Laser at CLARA

TY  - CONF
AU  - Pollard, A.E.
AU  - Dunning, D.J.
AU  - Okell, W.A.
AU  - Snedden, E.W.
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 Approach to Temporal Pulse Shaping for the Photoinjector Laser at CLARA
J2  - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022
CY  - Bangkok, Thailand
T2  - International Particle Accelerator Conference
T3  - 13
LA  - english
AB  - The temporal profile of the electron bunch is of critical importance in accelerator areas such as free-electron lasers and novel acceleration. In FELs, it strongly influences factors including efficiency and the profile of the photon pulse generated for user experiments, while in novel acceleration techniques it contributes to enhanced interaction of the witness beam with the driving electric field. Work is in progress at the CLARA facility at Daresbury Laboratory on temporal shaping of the ultraviolet photoinjector laser, using a fused-silica acousto-optic modulator. Generating a user-defined (programmable) time-domain target profile requires finding the corresponding spectral phase configuration of the shaper; this is a non-trivial problem for complex pulse shapes. Physically informed machine learning models have shown great promise in learning complex relationships in physical systems, and so we apply machine learning techniques here to learn the relationships between the spectral phase and the target temporal intensity profiles. Our machine learning model extends the range of available photoinjector laser pulse shapes by allowing users to achieve physically realisable configurations for arbitrary temporal pulse shapes.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 2917
EP  - 2920
KW  - laser
KW  - network
KW  - target
KW  - experiment
KW  - electron
DA  - 2022/07
PY  - 2022
SN  - 2673-5490
SN  - 978-3-95450-227-1
DO  - doi:10.18429/JACoW-IPAC2022-THPOTK061
UR  - https://jacow.org/ipac2022/papers/thpotk061.pdf
ER  -