SensitivityEstimator¶

class gammapy.spectrum.SensitivityEstimator(irf, livetime, slope=2.0, alpha=0.2, sigma=5.0, gamma_min=10.0, bkg_sys=0.05)[source]¶

Bases: object

Estimate differential sensitivity.

Uses a 1D spectral analysis and on / off measurement.

Parameters:

irf : CTAPerf

IRF object

livetime : Quantity

Livetime (object with the units of time), e.g. 5*u.h

slope : float, optional

Index of the spectral shape (Power-law), should be positive (>0)

alpha : float, optional

On/OFF normalisation

sigma : float, optional

Minimum significance

gamma_min : float, optional

Minimum number of gamma-rays

bkg_sys : float, optional

Fraction of Background systematics relative to the number of ON counts

Notes

For the moment, only the differential point-like sensitivity is computed at a fixed offset. This class allows to determine for each reconstructed energy bin the flux associated to the number of gamma-ray events for which the significance is sigma, and being larger than gamma_min and bkg_sys percent larger than the number of background events in the ON region.

Examples

Compute and plot a sensitivity curve for CTA:

from gammapy.irf import CTAPerf
from gammapy.spectrum import SensitivityEstimator

filename = '$GAMMAPY_DATA/cta/perf_prod2/point_like_non_smoothed/South_5h.fits.gz'
irf = CTAPerf.read(filename)
sensitivity_estimator = SensitivityEstimator(irf=irf, livetime='5h')
sensitivity_estimator.run()
print(sensitivity_estimator.results_table)

Further examples in cta_sensitivity.html .

Attributes Summary

results_table Results table (Table).

Methods Summary

run() Run the computation.

Attributes Documentation

results_table¶: Results table (Table).

Methods Documentation

run()[source]¶: Run the computation.