A circular statistical method for extracting rotation measures

We propose a new method for the extraction of rotation measures from spectral polarization data. The method is based on maximum likelihood analysis and takes into account the circular nature of the polarization data. The method is unbiased and statistically more efficient than the standard chi (2) procedure. We also find that the method is computationally much more convenient than the standard chi (2) procedure if the number of data points is very large. We find that for most sources the method gives results very close to the standard chi (2) minimization procedure. We give results for all the cases for which the method gives significantly different results from chi (2) minimization. We also make a lambda (3) fit to the data in order to extract non-Faraday rotation behaviour for those sources for which a large number of data points are available.