ExcessMapEstimator

class gammapy.estimators.ExcessMapEstimator(correlation_radius='0.1 deg', n_sigma=1, n_sigma_ul=3, selection_optional='all', energy_edges=None, apply_mask_fit=False)

Bases: gammapy.estimators.Estimator

Computes correlated excess, sqrt TS (i.e. Li-Ma significance) and errors for MapDatasets.

Parameters

correlation_radius~astropy.coordinate.Angle

correlation radius to use

n_sigmafloat

Confidence level for the asymmetric errors expressed in number of sigma. Default is 1.

n_sigma_ulfloat

Confidence level for the upper limits expressed in number of sigma. Default is 3.

selection_optionallist of str

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

• “errn-errp”: estimate asymmetric errors.

• “ul”: estimate upper limits.

By default all additional quantities are estimated.

energy_edgesQuantity

Energy edges of the target excess maps bins.

apply_mask_fitBool

Apply a mask for the computation. A mask_fit must be present on the input dataset

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 single dataset.
get_sqrt_ts(ts, norm)	Compute sqrt(TS) value.
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 single dataset.

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

Parameters

datasetMapDataset

Map dataset

static get_sqrt_ts(ts, norm)

Compute sqrt(TS) value.

Compute sqrt(TS) as defined by:

\[\begin{split}\sqrt{TS} = \left \{ \begin{array}{ll} -\sqrt{TS} & : \text{if} \ norm < 0 \\ \sqrt{TS} & : \text{else} \end{array} \right.\end{split}\]

Parameters

tsndarray

TS value.

normndarray

norm value

Returns

——-

sqrt_tsndarray

Sqrt(TS) value.

run(dataset)

Compute correlated excess, Li & Ma significance and flux maps

Parameters

datasetMapDataset or MapDatasetOnOff

input dataset

Returns

imagesdict

Dictionary containing result correlated maps. Keys are:

• counts : correlated counts map

• background : correlated background map

• excess : correlated excess map

• ts : TS map

• sqrt_ts : sqrt(delta TS), or Li-Ma significance map

• err : symmetric error map (from covariance)

• flux : flux map. An exposure map must be present in the dataset to compute flux map

• errn : negative error map

• errp : positive error map

• ul : upper limit map