A post-merger enhancement only in star-forming Type 2 Seyfert galaxies: the deep learning view

Avirett-Mackenzie, M S; Villforth, C; Huertas-Company, M; Wuyts, S; Alexander, D M; Bonoli, S; Lapi, A; Lopez, I E; Ramos Almeida, C; Shankar, F

Abstract

Supermassive black holes require a reservoir of cold gas at the centre of their host galaxy in order to accrete and shine as active galactic nuclei (AGN). Major mergers have the ability to drive gas rapidly inwards, but observations trying to link mergers with AGN have found mixed results due to the difficulty of consistently identifying galaxy mergers in surveys. This study applies deep learning to this problem, using convolutional neural networks trained to identify simulated post-merger galaxies from survey-realistic imaging. This provides a fast and repeatable alternative to human visual inspection. Using this tool, we examine a sample of ∼8500 Seyfert 2 galaxies (L[O III] ∼ 1038.5−42 erg s−1) at z < 0.3 in the Sloan Digital Sky Survey and find a merger fraction of 2.19+0.21 −0.17 per cent compared with inactive control galaxies, in which we find a merger fraction of 2.96+0.26 −0.20 per cent, indicating an overall lack of mergers among AGN hosts compared with controls. However, matching the controls to the AGN
hosts in stellar mass and star formation rate reveals that AGN hosts in the star-forming blue cloud exhibit a ∼2 × merger enhancement over controls, while those in the quiescent red sequence have significantly lower relative merger fractions, leading to the observed overall deficit due to the differing M∗–SFR distributions. We conclude that while mergers are not the dominant trigger of all low-luminosity, obscured AGN activity in the nearby Universe, they are more important to AGN fuelling in galaxies with higher cold gas mass fractions as traced through star formation.

Citation

Avirett-Mackenzie, M. S., Villforth, C., Huertas-Company, M., Wuyts, S., Alexander, D. M., Bonoli, S., …Shankar, F. (2024). A post-merger enhancement only in star-forming Type 2 Seyfert galaxies: the deep learning view. Monthly Notices of the Royal Astronomical Society, 528(4), 6915-6933. https://doi.org/10.1093/mnras/stae183

• Journal Article Type: Article
• Acceptance Date: Jan 10, 2024
• Online Publication Date: Feb 22, 2024
• Publication Date: 2024-03
• Deposit Date: Mar 12, 2024
• Publicly Available Date: Mar 12, 2024
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Royal Astronomical Society
• Peer Reviewed: Peer Reviewed
• Volume: 528
• Issue: 4
• Pages: 6915-6933
• DOI: https://doi.org/10.1093/mnras/stae183
• Public URL: https://durham-repository.worktribe.com/output/2326317