构建首个用于弯曲射电星系分类的专用数据集，助力研究星系团演化。
RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification
- 基于知名射电巡天构建数据集，聚焦NAT与WAT两类弯曲射电星系
- ConvNeXT在分类任务中表现最优，对NAT和WAT均取得最高F1分数
- 适合从事天体物理、机器学习在天文应用的研究者使用
我们提出一个面向天文学观测中弯曲射电活动星系核（AGN）分类的新型机器学习数据集。弯曲射电AGN以其弯曲喷流结构为特征，对理解星系团动力学、星系际介质内相互作用及AGN物理具有重要意义。然而，由于缺乏专用数据集与基准，其分类仍具挑战。为此，我们基于一项广受认可的射电天文巡天，构建了涵盖窄角尾（NAT）与宽角尾（WAT）类别的数据集，并提供详细的数据处理流程。我们进一步评估了先进深度学习模型在此数据集上的性能，包括卷积神经网络（CNNs）与基于Transformer的架构。结果表明，先进机器学习模型在分类任务中表现有效，其中ConvNeXT在两类源上均达到最高F1分数。通过共享该数据集与基准，我们旨在推动活动星系核分类、星系团环境及星系演化的研究进展。
原文摘要 · Abstract (English)
We introduce a novel machine learning dataset tailored for the classification of bent radio active galactic nuclei (AGN) in astronomical observations. Bent radio AGN, distinguished by their curved jet structures, provide critical insights into galaxy cluster dynamics, interactions within the intracluster medium, and the broader physics of AGN. Despite their astrophysical significance, the classification of bent radio AGN remains a challenge due to the scarcity of specialized datasets and benchmarks. To address this, we present a dataset, derived from a well-recognized radio astronomy survey, that is designed to support the classification of NAT (Narrow-Angle Tail) and WAT (Wide-Angle Tail) categories, along with detailed data processing steps. We further evaluate the performance of state-of-the-art deep learning models on the dataset, including Convolutional Neural Networks (CNNs), and transformer-based architectures. Our results demonstrate the effectiveness of advanced machine learning models in classifying bent radio AGN, with ConvNeXT achieving the highest F1-scores for both NAT and WAT sources. By sharing this dataset and benchmarks, we aim to facilitate the advancement of research in AGN classification, galaxy cluster environments and galaxy evolution.
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