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.
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机译：我们提出了一种从光谱极化数据中提取旋转量度的新方法。该方法基于最大似然分析，并考虑了极化数据的循环特性。与标准的\ chi ^ 2 $过程相比，该方法无偏见且在统计上更有效。我们还发现，如果数据点的数量很大，则该方法在计算上比标准的\\ chi ^ 2 $过程要快得多。
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