Beam divergence

By default, the incident beam is perfectly monochromatic and collimated. Here we show how to set finite distributions of wavelengths and of incident angles.

  • The wavelength follows a log-normal distribution around the mean value of 0.1 nm with a scale parameter equal to $0.1$.
  • Both incident angles follow a Gaussian distribution around the average values $\alpha_i = 0.2 ^{\circ}$ and $\varphi_i = 0^{\circ}$, respectively and $\sigma_{\alpha_i} = \sigma_{\varphi_i} = 0.1^{\circ}$.

The DWBA simulation is shown for a standard sample model:

  • The sample is composed of monodisperse cylinders deposited on a substrate.
  • The cylinders are dilute and distributed at random, hence there is no interference between scattered waves.

Real-space model

Intensity image

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#!/usr/bin/env python3
"""
Cylinder form factor in DWBA with beam divergence
"""
import bornagain as ba
from bornagain import ba_plot as bp, deg, nm


def get_sample():
    from bornagain import std_samples
    return std_samples.cylinders()


def get_simulation(sample):
    beam = ba.Beam(1, 0.1*nm, ba.Direction(0.2*deg, 0))
    detector = ba.SphericalDetector(bp.simargs['n'], 2*deg, 1*deg, 1*deg)
    simulation = ba.ScatteringSimulation(beam, sample, detector)
    distr_1 = ba.DistributionLogNormal(0.1*nm, 0.1)
    simulation.addParameterDistribution(
        ba.ParameterDistribution.BeamWavelength, distr_1, 5, 0)
    distr_2 = ba.DistributionGaussian(0.2*deg, 0.1*deg)
    simulation.addParameterDistribution(
        ba.ParameterDistribution.BeamInclinationAngle, distr_2, 5, 0)
    distr_3 = ba.DistributionGaussian(0, 0.1*deg)
    simulation.addParameterDistribution(
        ba.ParameterDistribution.BeamAzimuthalAngle, distr_3, 5, 0)
    return simulation


if __name__ == '__main__':
    bp.parse_args(sim_n=100)
    sample = get_sample()
    simulation = get_simulation(sample)
    result = simulation.simulate()
    bp.plot_simulation_result(result)
Examples/scatter2d/BeamDivergence.py