get_combined_significance_maps#
- gammapy.estimators.utils.get_combined_significance_maps(estimator, datasets)[source]#
Computes excess and significance for a set of datasets. The significance computation assumes that the model contains one degree of freedom per valid energy bin in each dataset. This method implemented here is valid under the assumption that the TS in each independent bin follows a Chi2 distribution, then the sum of the TS also follows a Chi2 distribution (with the sum of degree of freedom).
See, Zhen (2014): https://www.sciencedirect.com/science/article/abs/pii/S0167947313003204, Lancaster (1961): https://onlinelibrary.wiley.com/doi/10.1111/j.1467-842X.1961.tb00058.x
- Parameters:
- estimator
ExcessMapEstimatororTSMapEstimator Excess Map Estimator or TS Map Estimator
- dataset
Datasets Datasets containing only
MapDataset.
- estimator
- Returns:
- resultsdict
Dictionary with keys : - “significance” : joint significance map. - “df” : degree of freedom map (one norm per valid bin). - “npred_excess” : summed excess map. - “estimator_results” : dictionary containing the estimator results for each dataset.