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Supermassive black holes require a reservoir of cold gas at the centre oftheir host galaxy in order to accrete and shine as active galactic nuclei(AGN). Major mergers have the ability to drive gas rapidly inwards, butobservations trying to link mergers with AGN have found mixed results due tothe difficulty of consistently identifying galaxy mergers in surveys. Thisstudy applies deep learning to this problem, using convolutional neuralnetworks trained to identify simulated post-merger galaxies fromsurvey-realistic imaging. This provides a fast and repeatable alternative tohuman visual inspection. Using this tool, we examine a sample of ~8500 Seyfert2 galaxies (L[OIII] ~ $10^{38.5 - 42}$ erg/s) at z < 0.3 in the Sloan DigitalSky Survey and find a merger fraction of $2.19_{-0.17}^{+0.21}$% compared withinactive control galaxies, in which we find a merger fraction of$2.96_{-0.20}^{+0.26}$%, indicating an overall lack of mergers among AGN hostscompared with controls. However, matching the controls to the AGN hosts instellar mass and star formation rate reveals that AGN hosts in the star-formingblue cloud exhibit a ~$2\times$ merger enhancement over controls, while thosein the quiescent red sequence have significantly lower relative mergerfractions, leading to the observed overall deficit due to the differing$M_{\ast} - $SFR distributions. We conclude that while mergers are not thedominant trigger of all low-luminosity, obscured AGN activity in the nearbyUniverse, they are more important to AGN fuelling in galaxies with higher coldgas mass fractions as traced through star formation.

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