Deep learning model RGC 1.0 classifies radio galactic nuclei

Researchers have developed RGC 1.0, a novel semi-supervised deep learning model designed to classify radio active galactic nuclei (RAGNs). This model, integrated with BYOL and an E(2)-equivariant steerable CNN, was trained on a new dataset called FIRST-2060, which includes 2060 labeled RAGNs and 20,000 unlabeled sources. The RGC model demonstrates strong performance, comparable to supervised baselines, and uniquely traces the morphological structures of RAGNs in its attention analysis, enabling more detailed environmental studies. AI

IMPACT This model advances AI applications in astrophysics by enabling more precise classification of celestial objects, potentially leading to new discoveries about galaxy environments.

RANK_REASON This is a research paper detailing a new deep learning model and dataset for astrophysical classification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• ConvNeXT
• E(2)-equivariant steerable CNN
• FIRST-2060
• Khan Muhammad Bin Asad
• RGC 1.0

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →