Identifying AGN host galaxies with convolutional neural networks

TL;DR

This paper develops a convolutional neural network to efficiently identify active galactic nuclei host galaxies from imaging data, offering a faster alternative to traditional spectroscopic methods and aiding future large-scale surveys.

Contribution

The study introduces a CNN trained on 210,000 SDSS galaxies to classify AGN hosts, demonstrating its effectiveness on spectrally classified galaxies and revealing morphological correlations.

Findings

CNN achieves high classification accuracy.

Morphological features correlate with AGN activity.

Method enables rapid AGN identification for large surveys.

Abstract

Active galactic nuclei (AGN) are supermassive black holes with luminous accretion disks found in some galaxies, and are thought to play an important role in galaxy evolution. However, traditional optical spectroscopy for identifying AGN requires time-intensive observations. We train a convolutional neural network (CNN) to distinguish AGN host galaxies from non-active galaxies using a sample of 210,000 Sloan Digital Sky Survey galaxies. We evaluate the CNN on 33,000 galaxies that are spectrally classified as composites, and find correlations between galaxy appearances and their CNN classifications, which hint at evolutionary processes that affect both galaxy morphology and AGN activity. With the advent of the Vera C. Rubin Observatory, Nancy Grace Roman Space Telescope, and other wide-field imaging telescopes, deep learning methods will be instrumental for quickly and reliably shortlisting AGN samples for future analyses.