The galaxy morphology-density relation in the EAGLE simulation

TL;DR

This study uses a convolutional neural network to analyze galaxy morphologies in the EAGLE simulation, revealing how environment influences galaxy types through processes like gas stripping and mergers.

Contribution

First application of CNN-based visual classification to simulate galaxy morphologies, linking environment to galaxy evolution in cosmological simulations.

Findings

EAGLE reproduces observed morphology-density and morphology-mass relations.

Gas stripping transforms spirals into lenticulars in dense environments.

Merger-induced feedback contributes to lenticular formation in low-density regions.

Abstract

The optical morphology of galaxies is strongly related to galactic environment, with the fraction of early-type galaxies increasing with local galaxy density. In this work we present the first analysis of the galaxy morphology-density relation in a cosmological hydrodynamical simulation. We use a convolutional neural network, trained on observed galaxies, to perform visual morphological classification of galaxies with stellar masses $M_\ast > 10^{10} \, \mathrm{M}_\odot$ in the EAGLE simulation into elliptical, lenticular and late-type (spiral/irregular) classes. We find that EAGLE reproduces both the galaxy morphology-density and morphology-mass relations. Using the simulations, we find three key processes that result in the observed morphology-density relation: (i) transformation of disc-dominated galaxies from late-type (spiral) to lenticular galaxies through gas stripping in high-density environments, (ii) formation of lenticular galaxies by merger-induced black hole feedback in low-density environments, and (iii) an increasing fraction of high-mass galaxies, which are more often elliptical galaxies, at higher galactic densities.