exosim.tasks.radiometric.compute_observation_efficiency_from_dead_time#

Classes#

ComputeObservationEfficiencyFromDeadTime

Task to compute observation efficiency based on detector dead time.

Module Contents#

class ComputeObservationEfficiencyFromDeadTime[source]#

Bases: exosim.tasks.radiometric.compute_observation_efficiency.ComputeObservationEfficiency

Task to compute observation efficiency based on detector dead time.

This class calculates the observation efficiency for each aperture in the radiometric table by considering the detector’s dead time. The observation efficiency is computed as the ratio of integration time to the sum of integration time and dead time:

\[efficiency = \frac{t_{int}}{t_{int} + t_{dead}}\]

Where: - \(t_{int}\) is the integration time for each aperture - \(t_{dead}\) is the detector dead time (constant for all apertures)

The dead time represents the period after each readout during which the detector cannot acquire new data, reducing the overall observational efficiency.

Parameters:

  • radiometric_table (astropy.table.QTable or array-like) – Table containing radiometric information. Must include columns: - ‘integration_time’: integration time for each aperture (astropy.units.Quantity, in s) - ‘ch_name’: channel name for filtering (if channel_name is specified)

  • description (dict) – Dictionary containing the channel description. Should include: - ‘radiometric’/’dead_time’: detector dead time (astropy.units.Quantity, in s) If not provided, assumes dead_time = 0 s (100% efficiency).

  • channel_name (str, optional) – Name of the channel to filter the table. If provided, only apertures matching this channel name are processed.

Returns:

Array of observation efficiency values (dimensionless) for each aperture in the filtered radiometric table. Values range from 0 to 1, where: - 1.0 = 100% efficiency (no dead time) - 0.5 = 50% efficiency (dead time equals integration time)

Return type:

float

Notes

This implementation accounts for detector-specific dead time effects, making it more accurate than constant efficiency models for detectors with significant readout overhead.

model(radiometric_table, description, channel_name)[source]#

Compute observation efficiency based on detector dead time.

This method calculates the observation efficiency for each aperture by considering the detector’s dead time. The efficiency is computed as: efficiency = integration_time / (integration_time + dead_time)

Parameters:

  • radiometric_table (astropy.table.QTable) – Table containing radiometric information for each aperture. Must include: - ‘integration_time’ column (astropy.units.Quantity, in s) - ‘ch_name’ column if channel_name is specified for filtering

  • description (dict) – Channel description dictionary. Should contain the dead time specification under description["radiometric"]["dead_time"] (astropy.units.Quantity, in s). If not present, assumes dead_time = 0 s (perfect efficiency).

  • channel_name (str or None) – Name of the specific channel to process. If provided, only apertures matching this channel name are considered. If None, all apertures in the table are processed.

Returns:

Array of observation efficiency values (dimensionless) for each aperture. Each value represents the fraction of time the detector is actively observing for that specific aperture’s integration time.

Return type:

float

Notes

  • Efficiency values range from 0 to 1 (0% to 100%)

  • Longer integration times result in higher efficiency (less impact from dead time)

  • If dead_time = 0, efficiency = 1.0 for all apertures

  • If dead_time equals integration_time, efficiency = 0.5 (50%)

  • Dead time is assumed constant across all apertures for a given detector