Threshold Sensitivity: Event Detection Near the Noise Floor

This tutorial investigates how discriminator threshold affects event recovery for weak signals close to baseline noise.

from FlowCyPy.units import ureg
from FlowCyPy import FlowCytometer
from FlowCyPy.fluidics import (
    Fluidics,
    FlowCell,
    ScattererCollection,
    populations,
    distributions,
    SampleFlowRate,
    SheathFlowRate,
)
from FlowCyPy.opto_electronics import (
    Detector,
    Digitizer,
    OptoElectronics,
    Amplifier,
    source,
    circuits,
)
from FlowCyPy.digital_processing import (
    DigitalProcessing,
    discriminator,
    peak_locator,
)

Step 1: Build a weak-signal simulation

flow_cell = FlowCell(
    sample_volume_flow=SampleFlowRate.MEDIUM.value,
    sheath_volume_flow=SheathFlowRate.MEDIUM.value,
    width=400 * ureg.micrometer,
    height=150 * ureg.micrometer,
)

scatterer_collection = ScattererCollection()

population = populations.SpherePopulation(
    name="Near-threshold population",
    medium_refractive_index=distributions.Delta(1.33),
    concentration=1e10 * ureg.particle / ureg.milliliter,
    diameter=distributions.RosinRammler(
        scale=100 * ureg.nanometer,
        shape=12,
    ),
    refractive_index=distributions.Normal(
        mean=1.40,
        standard_deviation=0.002,
        low_cutoff=1.33,
    ),
    sampling_method=populations.ExplicitModel(),
)

scatterer_collection.add_population(population)

scatterer_collection.dilute(100)

fluidics = Fluidics(
    scatterer_collection=scatterer_collection,
    flow_cell=flow_cell,
)

analog_processing = [
    circuits.BaselineRestorationServo(time_constant=10 * ureg.microsecond),
    circuits.BesselLowPass(cutoff_frequency=1 * ureg.megahertz, order=4, gain=2),
]

excitation_source = source.Gaussian(
    waist_z=10e-6 * ureg.meter,
    waist_y=60e-6 * ureg.meter,
    wavelength=405 * ureg.nanometer,
    optical_power=25 * ureg.milliwatt,
    rin=-140 * ureg.dB_per_Hz,
    bandwidth=10 * ureg.megahertz,
)

detectors = [
    Detector(
        name="side",
        phi_angle=90 * ureg.degree,
        numerical_aperture=1.1,
        responsivity=1 * ureg.ampere / ureg.watt,
    )
]

amplifier = Amplifier(
    gain=10 * ureg.volt / ureg.ampere,
    bandwidth=10 * ureg.megahertz,
    voltage_noise_density=0.001 * ureg.nanovolt / ureg.sqrt_hertz,
    current_noise_density=0.001 * ureg.femtoampere / ureg.sqrt_hertz,
)

digitizer = Digitizer(
    sampling_rate=60 * ureg.megahertz,
    bit_depth=14,
    use_auto_range=True,
    channel_range_mode="shared",
)

opto_electronics = OptoElectronics(
    digitizer=digitizer,
    detectors=detectors,
    source=excitation_source,
    amplifier=amplifier,
    analog_processing=analog_processing,
)

cytometer = FlowCytometer(
    fluidics=fluidics,
    background_power=0 * ureg.picowatt,
)

Step 2: Sweep discriminator threshold

threshold_values = ["2sigma", "3sigma", "4sigma", "5sigma"]
run_records = []

for threshold_value in threshold_values:
    digital_processing = DigitalProcessing(
        discriminator=discriminator.DynamicWindow(
            trigger_channel="side",
            threshold=threshold_value,
            pre_buffer=40,
            post_buffer=40,
            max_triggers=-1,
        ),
        peak_algorithm=peak_locator.GlobalPeakLocator(),
    )

    run_record = cytometer.run(
        opto_electronics=opto_electronics,
        digital_processing=digital_processing,
        run_time=1 * ureg.millisecond,
    )

    run_records.append((threshold_value, run_record))

Step 3: Compare recovered events

for threshold_value, run_record in run_records:
    print(f"Threshold: {threshold_value}")
    _ = run_record.plot_analog(figure_size=(12, 4))
    _ = run_record.plot_digital(figure_size=(12, 4), xlim=(0, 0.1 * ureg.millisecond))
    _ = run_record.plot_peaks(x=("side", "Height"))

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Threshold: 2sigma
Threshold: 3sigma
Threshold: 4sigma
Threshold: 5sigma