ParameterSensitivityEstimator#

class gammapy.estimators.ParameterSensitivityEstimator(parameter, null_value, n_sigma=5, n_free_parameters=None, rtol=0.01, max_niter=100)[source]#

Bases: object

Estimate the sensitivity to a given parameter.

Computes the TS distribution in the non-null hypothesis using the log likelihood of the Asimov dataset (i.e. a dataset with counts = npred) and the non-central chi2 distribution. Once the TS distribution under the testing hypothesis is known, one can compute the required parameter value to have 50% of measurements above a given significance threshold.

Parameters:

parameterParameter

Parameter to test

null_valuefloat or Parameter

Value of the parameter for the null hypothesis.

n_sigmaint, optional

Number of required significance level. Default is 5.

rtolfloat, optional

Relative precision of the estimate. Used as a stopping criterion. Default is 0.01.

max_niterint, optional

Maximal number of iterations used by the root finding algorithm. Default is 100.

References

• Cowan et al. (2011), “Asymptotic formulae for likelihood-based tests of new physics”

Attributes Summary

tag

Methods Summary

parameter_matching_significance(datasets)	Parameter value matching the target significance
run(datasets)	Parameter sensitivity given as the difference between value matching the target significance and the null value.

Attributes Documentation

tag = 'ParameterSensitivityEstimator'#

Methods Documentation

parameter_matching_significance(datasets)[source]#

Parameter value matching the target significance

run(datasets)[source]#

Parameter sensitivity given as the difference between value matching the target significance and the null value.