On similarity metrics evaluating the performance of mode decomposition in few-mode optical fibers

Mode decomposition refers to a set of techniques aimed to recover modal content in multimode optical fibers. In this Letter, we examine the appropriateness of the similarity metrics commonly used in experiments on mode decomposition in few-mode fibers. We show that the conventional Pearson correlation coefficient is often misleading and should not be used as the sole criterion for justifying decomposition performance in the experiment. We consider several alternatives to the correlation and propose another metric that most accurately reflects the discrepancy between complex mode coefficients, given received and recovered beam speckles. In addition, we show that such a metric enables transfer learning of deep neural networks on experimental data and tangibly ameliorates their performance.