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RIS citation export for TUPA41: Applications of Machine Learning in Photo-Cathode Injectors

TY  - CONF
AU  - Aslam, A.
AU  - Babzien, M.
AU  - Biedron, S.
ED  - Biedron, Sandra
ED  - Simakov, Evgenya
ED  - Milton, Stephen
ED  - Anisimov, Petr M.
ED  - Schaa, Volker R.W.
TI  - Applications of Machine Learning in Photo-Cathode Injectors
J2  - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022
CY  - Albuquerque, NM, USA
T2  - International Particle Accelerator Conference
T3  - 5
LA  - english
AB  - To configure a photoinjector to reproduce a given electron bunch with the desired characteristics, it is necessary to adjust the operating parameters with high precision. More or less, the fine tunability of the laser parameters are of extreme importance as we try to model further applications of the photoinjector. The laser pulse incident on the photocathode critically affects the electron bunch 3D phase space. Parameters such as the laser pulse transverse shape, total energy, and temporal profile must be controlled independently, any laser pulse variation over both short and long-time scales also requires correction. The ability to produce arbitrary laser intensity distributions enables better control of electron bunch transverse and longitudinal emittance by affecting the space-charge forces throughout the bunch. In an accelerator employing a photoinjector, electron optics in the beamline downstream are used to transport, manipulate, and characterize the electron bunch. The adjustment of the electron optics to achieve a desired electron bunch at the interaction point is a much better understood problem than laser adjustment, so this research emphasizes laser shaping.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 441
EP  - 442
KW  - laser
KW  - electron
KW  - controls
KW  - cathode
KW  - network
DA  - 2022/10
PY  - 2022
SN  - 2673-7000
SN  - 978-3-95450-232-5
DO  - doi:10.18429/JACoW-NAPAC2022-TUPA41
UR  - https://jacow.org/napac2022/papers/tupa41.pdf
ER  -