Branislav

For the selected sample of calibrators, with radio surface brightness ($Sigma$) and linear diameter ($D$) data, we reconstruct the probability density function of the data in the $Sigma D$ plane, called $Sigma D$ pdf map. The $Sigma D$ pdf map is calculated with bootstrap approach by resampling the calibrating sample data. Unlike standard linear fit based calibrations our method gives more robust results. A probability density distribution for $D$ is obtained for each particular $Sigma$ value. This also gives a probability distribution of the corresponding statistical distance. We calculate distances for extragalactic and Galactic SNR calibrating samples and compare the results with standard, fit based approaches. In addition, utilizing the $Sigma D$ pdf map we quantify the data grouping in the $Sigma D$ plane which can be very useful tool for the studies on $Sigma D$ evolution of supernova remnants.