Source code for gammapy.spectrum.sensitivity

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import astropy.units as u
from astropy.table import Column, Table
from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
from gammapy.stats import excess_matching_significance_on_off
from .core import SpectrumEvaluator

__all__ = ["SensitivityEstimator"]

[docs]class SensitivityEstimator: """Estimate differential sensitivity. Uses a 1D spectral analysis and on / off measurement. For a usage example see `cta_sensitivity.html <../notebooks/cta_sensitivity.html>`__ Parameters ---------- arf : `~gammapy.irf.EffectiveAreaTable` 1D effective area rmf : `~gammapy.irf.EnergyDispersion` energy dispersion table bkg : `~gammapy.spectrum.CountsSpectrum` the background array livetime : `~astropy.units.Quantity` Livetime (object with the units of time), e.g. 5*u.h index : float, optional Index of the spectral shape (Power-law), should be positive (>0) alpha : float, optional On/OFF normalization 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 ----- 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. """ def __init__( self, arf, rmf, bkg, livetime, index=2.0, alpha=0.2, sigma=5.0, gamma_min=10.0, bkg_sys=0.05, ): self.arf = arf self.rmf = rmf self.bkg = bkg self.livetime = u.Quantity(livetime).to("s") self.index = index self.alpha = alpha self.sigma = sigma self.gamma_min = gamma_min self.bkg_sys = bkg_sys self._results_table = None @property def results_table(self): """Results table (`~astropy.table.Table`).""" return self._results_table
[docs] def run(self): """Run the computation.""" # TODO: let the user decide on energy binning # then integrate bkg model and gamma over those energy bins. energy = bkg_counts = ("1/s") * self.livetime).value excess_counts = excess_matching_significance_on_off( n_off=bkg_counts / self.alpha, alpha=self.alpha, significance=self.sigma ) is_gamma_limited = excess_counts < self.gamma_min excess_counts[is_gamma_limited] = self.gamma_min model = PowerLawSpectralModel( index=self.index, amplitude="1 cm-2 s-1 TeV-1", reference="1 TeV" ) # TODO: simplify the following computation predictor = SpectrumEvaluator( SkyModel(spectral_model=model), aeff=self.arf, edisp=self.rmf, livetime=self.livetime, ) counts = predictor.compute_npred().data phi_0 = excess_counts / counts dnde_model = model(energy=energy) diff_flux = (phi_0 * dnde_model * energy ** 2).to("erg / (cm2 s)") # TODO: take self.bkg_sys into account # and add a criterion 'bkg sys' criterion = [] for idx in range(len(energy)): if is_gamma_limited[idx]: c = "gamma" else: c = "significance" criterion.append(c) table = Table( [ Column( data=energy, name="energy", format="5g", description="Reconstructed Energy", ), Column( data=diff_flux, name="e2dnde", format="5g", description="Energy squared times differential flux", ), Column( data=excess_counts, name="excess", format="5g", description="Number of excess counts in the bin", ), Column( data=bkg_counts, name="background", format="5g", description="Number of background counts in the bin", ), Column( data=criterion, name="criterion", description="Sensitivity-limiting criterion", ), ] ) self._results_table = table return table