ExcessMapEstimator

class gammapy.estimators.ExcessMapEstimator(correlation_radius='0.1 deg', n_sigma=1, n_sigma_ul=2, selection_optional=None, energy_edges=None, correlate_off=True, spectral_model=None)

Bases: gammapy.estimators.core.Estimator

Computes correlated excess, significance and error maps from a map dataset.

If a model is set on the dataset the excess map estimator will compute the excess taking into account the predicted counts of the model.

Note

By default the excess estimator correlates the off counts as well to avoid artifacts at the edges of the FoV for stacked on-off datasets. However when the on-off dataset has been derived from a ring background estimate, this leads to the off counts being correlated twice. To avoid artifacts and the double correlation, the ExcessMapEstimator has to be applied per dataset and the resulting maps need to be stacked, taking the FoV cut into account.

Parameters

correlation_radius~astropy.coordinate.Angle

correlation radius to use

n_sigmafloat

Confidence level for the asymmetric errors expressed in number of sigma.

n_sigma_ulfloat

Confidence level for the upper limits expressed in number of sigma.

selection_optionallist of str

Which additional maps to estimate besides delta TS, significance and symmetric error. Available options are:

• “all”: all the optional steps are executed

• “errn-errp”: estimate asymmetric errors.

• “ul”: estimate upper limits.

Default is None so the optional steps are not executed.

energy_edgeslist of Quantity

Edges of the target maps energy bins. The resulting bin edges won’t be exactly equal to the input ones, but rather the closest values to the energy axis edges of the parent dataset. Default is None: apply the estimator in each energy bin of the parent dataset. For further explanation see Estimators (DL4 to DL5, and DL6).

correlate_offbool

Correlate OFF events. Default is True.

spectral_modelSpectralModel

Spectral model used for the computation of the flux map. If None, a Power Law of index 2 is assumed (default).

Examples

>>> from gammapy.datasets import MapDataset
>>> from gammapy.estimators import ExcessMapEstimator
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> estimator = ExcessMapEstimator(correlation_radius="0.1 deg")
>>> result = estimator.run(dataset)
>>> print(result)
FluxMaps
--------

  geom                   : WcsGeom
  axes                   : ['lon', 'lat', 'energy']
  shape                  : (320, 240, 1)
  quantities             : ['npred', 'npred_excess', 'counts', 'ts', 'sqrt_ts', 'norm', 'norm_err']  # noqa: E501
  ref. model             : pl
  n_sigma                : 1
  n_sigma_ul             : 2
  sqrt_ts_threshold_ul   : 2
  sed type init          : likelihood

Attributes Summary

config_parameters	Config parameters
correlation_radius
selection_optional
tag

Methods Summary

copy()	Copy estimator
estimate_excess_map(dataset)	Estimate excess and ts maps for a single dataset.
estimate_exposure_reco_energy(dataset, ...)	Estimate exposure map in reconstructed energy for a single dataset
estimate_kernel(dataset)	Get the convolution kernel for the input dataset.
estimate_mask_default(dataset)	Get mask used by the estimator.
run(dataset)	Compute correlated excess, Li & Ma significance and flux maps

Attributes Documentation

config_parameters

Config parameters

correlation_radius

selection_optional

tag = 'ExcessMapEstimator'

Methods Documentation

copy()

Copy estimator

estimate_excess_map(dataset)

Estimate excess and ts maps for a single dataset.

If exposure is defined, a flux map is also computed.

Parameters

datasetMapDataset

Map dataset

estimate_exposure_reco_energy(dataset, kernel, mask)

Estimate exposure map in reconstructed energy for a single dataset

assuming the given spectral_model shape.

Parameters

datasetMapDataset

Map dataset

kernelTophat2DKernel

Kernel

maskMap

Mask map

Returns

reco_exposureMap

Reconstructed exposure map

estimate_kernel(dataset)

Get the convolution kernel for the input dataset.

Parameters

datasetMapDataset

Input dataset.

Returns

kernelTophat2DKernel

Kernel

static estimate_mask_default(dataset)

Get mask used by the estimator.

Parameters

datasetMapDataset

Input dataset.

Returns

maskMap

Mask map

run(dataset)

Compute correlated excess, Li & Ma significance and flux maps

If a model is set on the dataset the excess map estimator will compute the excess taking into account the predicted counts of the model.

Parameters

datasetMapDataset or MapDatasetOnOff

Map dataset

Returns

mapsFluxMaps

Flux maps