Seeded self-modulation

This is an example of seeded self-modulation of a particle beam. The run reproduces results shown in Fig.2 of the paper where the self-modulation effect was first discovered. The results do not exactly coincide with those in the paper because the original figure was produced using a fluid plasma solver and much wider beam particles.

• Code launcher: original-ssm.py.

• Post-processing (Jupyter notebook): original-ssm-figs.ipynb and images that it produces:

Code launcher:

from lcode.simulation import Simulation
from lcode.diagnostics import FXiDiag, FXiType, OutputType, ParticlesDiag
from lcode.diagnostics import SliceDiag, SliceType, SliceValue


# Set parameters of the solver:
config = {
    'geometry': 'circ',
    'processing-unit-type': 'cpu',

    'window-width': 5,
    'transverse-step': 0.02,
    'window-length': 45,
    'xi-step': 0.02,
    'time-limit': 7500.1,
    'time-step': 5,
    
    'plasma-particles-per-cell': 10,
    'ion-model': 'background',

}

# Set beams
beams = {
    'current': -0.03, 'particles_in_layer': 250,
    'default': {'angspread':2e-4, 'energy': 1000},
    'driver': {'xishape':'l', 'length': 40, 'radius': 2},
    'witness': {'xishape':'l', 'length': 3, 'radius': 0.2}
}

# Set diagnostics
diag = [FXiDiag(
            output_period=100, 
            output_type=OutputType.NUMBERS,
            f_xi=FXiType.Ez | FXiType.rho_beam),
        SliceDiag(
            SliceType.XI_X,
            output_period=100, 
            output_type=OutputType.NUMBERS,
            slice_value = SliceValue.rho_beam | SliceValue.ne)
]

sim = Simulation(config=config, diagnostics=diag,
                 beam_parameters=beams)

sim.step()