2025 workshop program
Morphological Classification of Jets in Active Galactic Nuclei
The work
Abstract
This work explores the application of machine learning techniques to classifying active galactic nuclei (AGN) with jets based on Very-Long-Baseline Interferometry (VLBI) observations at frequencies 1–90 GHz. Building upon previous work by Fanaroff and Riley, who classified relativistic jets in radio galaxies on kiloparsec scales, we extend this classification to parsec scales, closer to the central supermassive black hole (SMBH). This approach enables detailed study of jet spatial structures and can help enhance accuracy in global positioning systems. We define four morphological classes: single Gaussian source, double Gaussian source, and sources with single or double-sided jets. Synthetic models of AGN jets were generated to create a training dataset for a convolutional neural network (CNN). The CNN was trained on these synthetic data and subsequently applied to classify 130 thousand AGN jet images from the Astrogeo database. The distribution of images into designated classes, predicted by CNN, qualitatively matches the expected outcome. In the near future, this CNN model will become the core of a public online tool for scientists, providing search for morphologically similar images of AGNs.