A Circular Statistical Method for Extracting Rotation Measures

We propose a new method for the extraction of Rotation Measure 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 faster than the standard $\chi^2$ procedure if the number of data points are very large.