Faraday complexity describes whether a spectropolarimetric observation has simple or complex magnetic structure. Quickly determining the Faraday complexity of a spectropolarimetric observation is important for processing large, polarised radio surveys. Finding simple sources lets us build rotation measure grids, and finding complex sources lets us follow these sources up with slower analysis techniques or further observations. We introduce five features that can be used to train simple, interpretable machine learning classifiers for estimating Faraday complexity. We train logistic regression and extreme gradient boosted tree classifiers on simulated polarised spectra using our features, analyse their behaviour, and demonstrate our features are effective for both simulated and real data. This is the first application of machine learning methods to real spectropolarimetry data. With 95% accuracy on simulated ASKAP data and 90% accuracy on simulated ATCA data, our method performs comparably to state-of-the-art convolutional neural networks while being simpler and easier to interpret. Logistic regression trained with our features behaves sensibly on real data and its outputs are useful for sorting polarised sources by apparent Faraday complexity.

中文翻译：

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可解释的法拉第复杂度分类

法拉第复杂度描述了光谱极化观测是否具有简单或复杂的磁结构。快速确定光谱极化观测的法拉第复杂度对于处理大型极化无线电勘测非常重要。寻找简单的来源可以让我们建立旋转测量网格，找到复杂的来源可以让我们用较慢的分析技术或进一步的观察来跟踪这些来源。我们介绍了五个可用于训练简单、可解释的机器学习分类器以估计法拉第复杂度的特征。我们使用我们的特征在模拟偏振光谱上训练逻辑回归和极端梯度增强树分类器，分析它们的行为，并证明我们的特征对模拟和真实数据都有效。这是机器学习方法首次应用于实际光谱偏振数据。我们的方法在模拟 ASKAP 数据上的准确度为 95%，在模拟的 ATCA 数据上准确度为 90%，其性能与最先进的卷积神经网络相当，同时更简单、更容易解释。使用我们的特征训练的逻辑回归在真实数据上表现良好，其输出可用于通过明显的法拉第复杂度对极化源进行分类。