RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification
This work addresses the challenge of bent radio AGN classification for astronomers, but it is incremental as it primarily provides a new dataset and benchmarks using existing methods.
The authors tackled the problem of classifying bent radio active galactic nuclei (AGN) by introducing a novel dataset derived from a radio astronomy survey, and they demonstrated that advanced machine learning models, particularly ConvNeXT, achieved the highest F1-scores for NAT and WAT categories.
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.