Speaker
Description
End-to-end simulations of intense relativistic electron beams generated by linear induction accelerators (LIA) often involve two-step processes whereby the beam creation is simulated using particle-in-cell (PIC) methods before a handoff to less computationally-expensive methods, e.g. beam envelope solvers, to determine sufficiently robust beam tunes. Because of this hand-off, fields that affect the PIC simulation of the A-K gap region are usually untouched during the tuning process. To allow for magnetic guide field optimization including magnets close to the A-K gap, a machine learning model of an LIA injector system is under development to allow for rapid end-to-end simulations of the electron beam for use in beam optimization problems, e.g. automated magnetic transport field tuning.
Footnotes
This work was done by Mission Support and Test Services, LLC, under
Contract No. DE-NA0003624 with the U.S. Department of Energy, the National Nuclear Security Administration’s Office of Defense Programs, and supported by the Site-Directed Research and Development Program. DOE/NV/03624--2065.
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