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SensitivityEstimator

class gammapy.scripts.SensitivityEstimator(irf, livetime, slope=2.0, alpha=0.2, sigma=5.0, gamma_min=10.0, bkg_sys=0.05, random=0)

Bases: object

Estimate differential sensitivity for the ON/OFF method, ie 1D analysis

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

random: : int, optional

Number of random trial to derive the number of gamma-rays

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’% larger than the number of background events in the ON region

TODO:

• make the computation for any source size
• compute the integral sensitivity
• Add options to use different spectral shape?

Examples

Compute and plot a sensitivity curve for CTA:

from gammapy.scripts import CTAPerf, SensitivityEstimator
filename = '$GAMMAPY_EXTRA/datasets/cta/perf_prod2/point_like_non_smoothed/South_5h.fits.gz'
irf = CTAPerf.read(filename)
sens = SensitivityEstimator(irf=irf, livetime='5h')
sens.run()
sens.print_results()
sens.plot()

Further examples in cta_sensitivity.html .

Attributes Summary

diff_sensi_table

Differential sensitivity table (Table).

Methods Summary

get_1TeV_differential_flux(excess_counts, …)	Compute the differential fluxes at 1 TeV.
get_bkg(bkg_rate)	Return the Background rate for each energy bin
get_excess(bkg_counts)	Compute the gamma-ray excess for each energy bin.
plot([ax])	Plot the sensitivity curve.
print_results()	Print results to the console.
run()	Run the algorithm to compute the differential sensitivity as explained in the document of the class.

Attributes Documentation

diff_sensi_table

Differential sensitivity table (Table).

Methods Documentation

get_1TeV_differential_flux(excess_counts, model, aeff, rmf)

Compute the differential fluxes at 1 TeV.

Parameters:

excess_counts : ndarray

Array of gamma-ray excess (bins in reconstructed energy)

model : SpectralModel

Spectral model

aeff : EffectiveAreaTable

Effective area

rmf : EnergyDispersion

RMF

Returns:

flux : Quantity

Array of 1TeV fluxes (bins in reconstructed energy)

get_bkg(bkg_rate)

Return the Background rate for each energy bin

Parameters:

bkg_rate : BgRateTable

Table of background rate

Returns:

rate : ndarray

Return an array of background counts (bins in reconstructed energy)

get_excess(bkg_counts)

Compute the gamma-ray excess for each energy bin.

Parameters:

bkg_counts : ndarray

Array of background counts (bins in reconstructed energy)

Returns:

count : ndarray

Array of gamma-ray excess (bins in reconstructed energy)

Notes

Find the number of needed gamma excess events using newtons method. Defines a function significance_on_off(x, off, alpha) - self.sigma and uses scipy.optimize.newton to find the x for which this function is zero.

plot(ax=None)

Plot the sensitivity curve.

print_results()

Print results to the console.

run()

Run the algorithm to compute the differential sensitivity as explained in the document of the class.