RGC: a radio AGN classifier based on deep learning. I. A semi-supervised multiclass model for VLA images
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Hossain, M. S. Shahal, M. S. H. Asad, K. M. B. Saikia, P. Khan, A. Akter, F. Ali, A. Amin, M. A. Guha, D. P. Jihad, M. O. B. Momen, A. Sen, S. Rahman, A. K. M. M. |
| author_facet | Hossain, M. S. Shahal, M. S. H. Asad, K. M. B. Saikia, P. Khan, A. Akter, F. Ali, A. Amin, M. A. Guha, D. P. Jihad, M. O. B. Momen, A. Sen, S. Rahman, A. K. M. M. |
| contents | Bent radio active galactic nuclei (RAGNs) -- wide-angle tails (WATs) and narrow-angle tails (NATs) -- trace dense environments in galaxy groups and clusters, yet no multiclass classifier simultaneously separates them from straight Fanaroff--Riley types (sFRI, sFRII) using visually inspected labels and unlabelled data. We release FIRST-2060, a four-class labelled dataset of 2060 RAGNs (sFRI, sFRII, WAT, NAT) constructed from three publicly available catalogues through multi-tier visual inspection, together with the semi-supervised RGC 1.0 model that leverages 20,000 unlabelled sources. We benchmark RGC against five supervised baselines. FIRST-2060 is provided in two preprocessing variants: $\mathbf{R}_{L1}$, which retains spurious sources, and $\mathbf{R}_{L2}$, from which they are removed. The RGC model integrates the self-supervised framework BYOL (Bootstrap Your Own Latent) with an $E(2)$-equivariant steerable CNN (E2CNN) encoder, pre-trained on the unlabelled data and fine-tuned on the labelled sets. All six models are evaluated with 5-fold cross-validation, Grad-CAM attention analysis, and controlled class-imbalance experiments. ConvNeXT ($M_1$) and RGC ($M_2$) form a top tier at macro-$F_1$ $0.80\pm0.02$ and $0.79\pm0.02$ respectively, a difference within one standard deviation. $M_2$ is the only model whose Grad-CAM contours consistently trace the morphological structure of RAGNs -- lobes, jets, and bends -- rather than defaulting to compact blobs or diffuse patterns. The four-class scheme introduced here enables WAT/NAT-resolved catalogues that can serve as environment probes and progenitor classifications for diffuse cluster radio emission. The complementary strengths of $M_1$ and $M_2$ -- in cross-type and within-type discrimination respectively -- suggest that an ensemble approach may offer a practical framework for survey-scale morphological catalogues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RGC: a radio AGN classifier based on deep learning. I. A semi-supervised multiclass model for VLA images Hossain, M. S. Shahal, M. S. H. Asad, K. M. B. Saikia, P. Khan, A. Akter, F. Ali, A. Amin, M. A. Guha, D. P. Jihad, M. O. B. Momen, A. Sen, S. Rahman, A. K. M. M. Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine Learning Bent radio active galactic nuclei (RAGNs) -- wide-angle tails (WATs) and narrow-angle tails (NATs) -- trace dense environments in galaxy groups and clusters, yet no multiclass classifier simultaneously separates them from straight Fanaroff--Riley types (sFRI, sFRII) using visually inspected labels and unlabelled data. We release FIRST-2060, a four-class labelled dataset of 2060 RAGNs (sFRI, sFRII, WAT, NAT) constructed from three publicly available catalogues through multi-tier visual inspection, together with the semi-supervised RGC 1.0 model that leverages 20,000 unlabelled sources. We benchmark RGC against five supervised baselines. FIRST-2060 is provided in two preprocessing variants: $\mathbf{R}_{L1}$, which retains spurious sources, and $\mathbf{R}_{L2}$, from which they are removed. The RGC model integrates the self-supervised framework BYOL (Bootstrap Your Own Latent) with an $E(2)$-equivariant steerable CNN (E2CNN) encoder, pre-trained on the unlabelled data and fine-tuned on the labelled sets. All six models are evaluated with 5-fold cross-validation, Grad-CAM attention analysis, and controlled class-imbalance experiments. ConvNeXT ($M_1$) and RGC ($M_2$) form a top tier at macro-$F_1$ $0.80\pm0.02$ and $0.79\pm0.02$ respectively, a difference within one standard deviation. $M_2$ is the only model whose Grad-CAM contours consistently trace the morphological structure of RAGNs -- lobes, jets, and bends -- rather than defaulting to compact blobs or diffuse patterns. The four-class scheme introduced here enables WAT/NAT-resolved catalogues that can serve as environment probes and progenitor classifications for diffuse cluster radio emission. The complementary strengths of $M_1$ and $M_2$ -- in cross-type and within-type discrimination respectively -- suggest that an ensemble approach may offer a practical framework for survey-scale morphological catalogues. |
| title | RGC: a radio AGN classifier based on deep learning. I. A semi-supervised multiclass model for VLA images |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2510.22190 |