An automated activity classification tool for optical galaxy spectra

TL;DR

This paper introduces an automated galaxy activity classification tool using support vector machines that accurately distinguishes various galaxy types from optical spectra without extensive preprocessing, suitable for high-redshift studies.

Contribution

The authors developed a novel, all-in-one diagnostic tool that outperforms existing methods by eliminating preprocessing steps and distinguishing all galaxy activity classes, including AGN types.

Findings

Achieves 83% overall accuracy in classifying galaxy activity.

Effectively distinguishes broad- and narrow-line AGN.

Maintains high performance without starlight subtraction.

Abstract

Reliable, versatile galaxy activity diagnostics are essential for understanding galaxy evolution. Traditional methods frequently necessitate extensive preprocessing, such as starlight subtraction and emission line deblending (e.g., H{\alpha} and [N II]), which can introduce substantial biases and uncertainties due to their model-dependent nature. In this work we developed an automated, diagnostic tool capable of distinguishing between star-forming (SF), active galactic nuclei (AGN), low-ionization nuclear emission-line regions (LINERs), composite, and passive galaxies. We developed a diagnostic tool based on a support vector machine trained on data from optical emission-line ratios and color selection criteria. From literature studies and exploring combinations of discriminatory feature schemes, we found that the equivalent widths of H{\beta}, [O III]{\lambda}5007, and H{\alpha}+[N II]{\lambda}6548,84 as key diagnostic features. Additionally, galaxies classified as AGN can be distinguished into broad- and narrow-line AGN by measuring the full quarter at the half-maximum of H{\alpha} and [N II] complex. We have developed a diagnostic tool that encompasses all activities of galaxies while achieving high performance scores across all of them. Our diagnostic achieves overall accuracy of 83% and recall of 79% for SF, 94% for AGN, 85% for LINER, 77% for composite, and 96% for passive galaxies. Our diagnostic tool significantly improves upon existing diagnostics as it eliminates the need for preprocessing (i.e., starlight subtraction or flux calibration) and spectral line fitting, includes all activity classes under one scheme, and distinguishes the two main AGN types. In addition, omitting starlight subtraction does not significantly reduce performance. Furthermore, Its narrow wavelength requirement enables use across a wide redshift range, making it ideal for high-z studies.