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Journal Article

Influence of proton bunch and plasma parameters on the AWAKE experiment

MPS-Authors

Moreira,  Mariana
Max Planck Institute for Physics, Max Planck Society and Cooperation Partners;

Vieira,  Jorge
Max Planck Institute for Physics, Max Planck Society and Cooperation Partners;

Muggli,  Patric
Max Planck Institute for Physics, Max Planck Society and Cooperation Partners;

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Citation

Moreira, M., Vieira, J., & Muggli, P. (2019). Influence of proton bunch and plasma parameters on the AWAKE experiment. Phys. Rev. Accel. Beams, 22, 031301. Retrieved from https://publications.mppmu.mpg.de/?action=search&mpi=MPP-2018-342.


Cite as: https://hdl.handle.net/21.11116/0000-0005-D76D-F
Abstract
We use particle-in-cell (PIC) simulations to study the effects of variations of the incoming 400 GeV proton bunch parameters on the amplitude and phase of the wakefields resulting from a seeded self-modulation (SSM) process. We find that these effects are largest during the growth of the SSM, i.e. over the first five to six meters of plasma with an electron density of $7 \times 10^{14}$ cm$^{-3}$. However, for variations of any single parameter by $\pm$5%, effects after the SSM saturation point are small. In particular, the phase variations correspond to much less than a quarter wakefield period, making deterministic injection of electrons (or positrons) into the accelerating and focusing phase of the wakefields in principle possible. We use the wakefields from the simulations and a simple test electron model to determine the injection position along the bunch and along the plasma leading to the largest energy gain. This analysis includes the dephasing of the electrons with respect to the wakefields that is expected during the growth of the SSM. We find that the optimum position along the proton bunch is at $\xi \approx -1.5 \; \sigma_{zb}$, and that the optimal range for injection along the plasma (for a highest final energy of $\sim$1.6 GeV after 10 m) is 5-6 m. The latter result is obtained from a PIC simulation that tests different injection points and is also used to validate the model mentioned above.