The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. One of the most crucial problems in this field is the automatic classification of extragalactic radio sources based on their morphologies. Most recent contributions in the field of morphological classification of extragalactic radio sources have proposed classifiers based on convolutional neural networks. Alternatively, this work proposes gradient boosting machine learning methods accompanied by principal component analysis as data-efficient alternatives to convolutional neural networks. Recent findings have shown the efficacy of gradient boosting methods in outperforming deep learning methods for classification problems with tabular data. The gradient boosting methods considered in this work are based on the XGBoost, LightGBM, and CatBoost implementations. This work also studies the effect of dataset size on classifier performance. A three-class classification problem is considered in this work based on the three main Fanaroff-Riley classes: class 0, class I, and class II, using radio sources from the Best-Heckman sample. All three proposed gradient boosting methods outperformed a state-of-the-art convolutional neural networks-based classifier using less than a quarter of the number of images, with CatBoost having the highest accuracy. This was mainly due to the superior accuracy of gradient boosting methods in classifying Fanaroff-Riley class II sources, with 3$\unicode{x2013}$4% higher recall.

翻译：射电天文学领域正见证着新型射电望远镜的投入使用导致每日数据量的激增。该领域最关键的问题之一是基于河外射电源的形态对其进行自动分类。近年来关于河外射电源形态分类的研究大多采用基于卷积神经网络的分类器。本研究则提出以梯度提升机器学习方法结合主成分分析作为卷积神经网络的数据高效替代方案。最新研究表明，在处理表格数据的分类问题时，梯度提升方法在性能上优于深度学习方法。本研究考察的梯度提升方法基于XGBoost、LightGBM和CatBoost三种实现，同时分析了数据集规模对分类器性能的影响。基于Best-Heckman样本中的射电源，本研究针对三类Fanaroff-Riley主要分类（0类、I类和II类）开展三分类问题研究。三种梯度提升方法在仅使用不到四分之一图像数量的情况下均优于当前最优的卷积神经网络分类器，其中CatBoost的准确率最高。这主要归因于梯度提升方法在Fanaroff-Riley II类源分类中的卓越准确率，其召回率提升了3–4%。