electrons_measured

optika.sensors.electrons_measured(photons_absorbed, wavelength, absorption=None, thickness_implant=<Quantity 2317. Angstrom>, cce_backsurface=0.21, temperature=<Quantity 300. K>, shape_random=None)

A random sample from the distribution of measured electrons given the number of photons absorbed by the light-sensitive layer of the sensor.

This function accounts for both Fano noise and recombination noise due to partial-charge collection.

Parameters:

• photons_absorbed (Quantity | AbstractScalar) – The number of photons absorbed by the light-sensitive layer of the sensor.

• wavelength (Quantity | ScalarArray) – The vacuum wavelength of the absorbed photons.

• absorption (None | Quantity | AbstractScalar) – The absorption coefficient of silicon at the given wavelength.

• thickness_implant (Quantity | AbstractScalar) – The thickness of the implant layer, where partial-charge collection occurs.

• cce_backsurface (Quantity | AbstractScalar) – The differential charge collection efficiency on the back surface of the sensor.

• temperature (Quantity | ScalarArray) – The temperature of the silicon detector. Default is room temperature.

• shape_random (None | dict[str, int]) – Additional shape used to specify the number of samples to draw.

Return type:

AbstractScalar

Examples

Plot the energy spectrum of 100 6 keV photons emitted from an Fe-55 radioactive source.

import matplotlib.pyplot as plt
import astropy.units as u
import astropy.visualization
import named_arrays as na
import optika

# Define the number of experiments to perform
num_experiments = 100000

# Define the expected number of photons
# for each experiment
photons_absorbed = (100 * u.photon).astype(int)

# Define the wavelength at which to sample the distribution
wavelength = 5.9 * u.keV
wavelength = wavelength.to(u.AA, equivalencies=u.spectral())

# Compute the actual number of electrons measured for each experiment
electrons = optika.sensors.electrons_measured(
    photons_absorbed=photons_absorbed,
    wavelength=wavelength,
    shape_random=dict(experiment=num_experiments),
)

# Define the histogram bins
step = 10
bins = na.arange(
    electrons.value.min()-step/2,
    electrons.value.max()+step/2,
    step=step,
    axis="bin",
) * u.electron

# Compute a histogram of resulting energy spectrum
hist = na.histogram(
    electrons,
    bins=bins,
    axis="experiment",
)

# Plot the histogram
with astropy.visualization.quantity_support():
    fig, ax = plt.subplots()
    line = na.plt.stairs(
      hist.inputs,
      hist.outputs,
      ax=ax,
    )

References to optika.sensors.electrons_measured
electrons_measured
electrons_measured_approx