RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification

2025-05-25

AI Summary

The classification of bent-radio active galactic nuclei (AGN)—specifically narrow-angle tail (NAT) and wide-angle tail (WAT) radio galaxies—lacks dedicated datasets and standardized benchmarks. Method: We construct the first high-quality, finely annotated image dataset for NAT/WAT classification, curated from authoritative radio sky surveys. We propose an end-to-end classification framework integrating ConvNeXT and Vision Transformer architectures, augmented with radio-image-specific preprocessing and data augmentation strategies. Contribution/Results: Experiments demonstrate that ConvNeXT achieves the highest F1-score on the NAT/WAT binary classification task, validating the efficacy of deep learning models in modeling complex radio morphologies. All data, code, and evaluation benchmarks are publicly released to support AGN morphological classification and galaxy cluster environmental studies.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Text Classification & Sentiment Analysis
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Classifying bent radio AGN with curved jet structures
• Addressing scarcity of specialized datasets for AGN classification
• Evaluating deep learning models for NAT and WAT categories

Innovation

• Novel dataset for bent AGN classification
• Deep learning models like CNNs, transformers
• ConvNeXT achieves highest F1-scores