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We study the morphologies of 3,964 galaxies and their progenitors with $M_\star > 10^{10} M_\odot$ in the reference EAGLE hydrodynamical simulation from redshifts $z=1$ to $z=0$, concentrating on the redshift evolution of the bar fraction. We apply two convolutional neural networks (CNNs) to classify 35,082 synthetic g-band images across 10 snapshots in redshift. We identify galaxies as either barred or unbarred, while also classifying each sample into one of four morphological types: elliptical (E), lenticular (S0), spiral (Sp), and irregular/miscellaneous (IrrM). We find that the bar fraction is roughly constant between $z = 0.0$ to $z = 0.5$ (32% to 33%), before exhibiting a general decline to 26% out to $z = 1$. The bar fraction is highest in spiral galaxies, from 49% at $z = 0$ to 39% at $z = 1$. The bar fraction in S0s is lower, ranging from 22% to 18%, with similar values for the miscellaneous category. Under 5% of ellipticals were classified as barred. We find that the bar fraction is highest in low mass galaxies ($M_\star \leq 10^{10.5} M_\odot$). Through tracking the evolution of galaxies across each snapshot, we find that some barred galaxies undergo episodes of bar creation, destruction and regeneration, with a mean bar lifetime of 2.24 Gyr. We further find that incidences of bar destruction are more commonly linked to major merging, while minor merging and accretion is linked to both bar creation and destruction., 15 pages, 18 figures, accepted for publication in MNRAS

Lenticular (S0) galaxies are galaxies that exhibit a bulge and disk component, yet lack any clear spiral features. With features considered intermediary between spirals and ellipticals, S0s have been proposed to be a transitional morphology, however their exact origin and nature is still debated. In this work, we study the redshift evolution of the S0 fraction out to $z \sim 1$ using deep learning to classify F814W ($i$-band) HST-ACS images of 85,378 galaxies in the Cosmological Evolution Survey (COSMOS). We classify galaxies into four morphological categories: elliptical (E), S0, spiral (Sp), and irregular/miscellaneous (IrrM). Our deep learning models, initially trained to classify SDSS images with known morphologies, have been successfully adapted to classify high-redshift COSMOS images via transfer learning and data augmentation, enabling us to classify S0s with superior accuracy. We find that there is an increase in the fraction of S0 galaxies with decreasing redshift, along with a corresponding reduction in the fraction of spirals. We find a bimodality in the mass distribution of our classified S0s, from which we find two separate S0s populations: high-mass S0s, which are mostly red and quiescent; and low-mass S0s, which are generally bluer and include both passive and star-forming S0s, the latter of which cannot solely be explained via the faded spiral formation pathway. We also find that the S0 fraction in high-mass galaxies begins rising at higher $z$ than in low-mass galaxies, implying that high-mass S0s evolved earlier., Comment: 18 pages, 18 figures, accepted for publication in MNRAS

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., 19 pages, 17 figures, published in MNRAS

Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological classification. We train and test several convolutional neural network architectures to classify the morphologies of galaxies in both a 3-class (elliptical, lenticular, spiral) and 4-class (+irregular/miscellaneous) schema with a dataset of 14034 visually-classified SDSS images. We develop a new CNN architecture that outperforms existing models in both 3 and 4-way classification, with overall classification accuracies of 83% and 81% respectively. We also compare the accuracies of 2-way / binary classifications between all four classes, showing that ellipticals and spirals are most easily distinguished (>98% accuracy), while spirals and irregulars are hardest to differentiate (78% accuracy). Through an analysis of all classified samples, we find tentative evidence that misclassifications are physically meaningful, with lenticulars misclassified as ellipticals tending to be more massive, among other trends. We further combine our binary CNN classifiers to perform a hierarchical classification of samples, obtaining comparable accuracies (81%) to the direct 3-class CNN, but considerably worse accuracies in the 4-way case (65%). As an additional verification, we apply our networks to a small sample of Galaxy Zoo images, obtaining accuracies of 92%, 82% and 77% for the binary, 3-way and 4-way classifications respectively., 18 pages, 18 figures, accepted for publication in MNRAS

Stellar bars are a common morphological feature of spiral galaxies. While it is known that they can form in isolation, or be induced tidally, few studies have explored the production of stellar bars in galaxy merging. We look to investigate bar formation in galaxy merging using methods from deep learning to analyse our N-body simulations. The primary aim is to determine the constraints on the mass ratio and orientations of merging galaxies that are most conducive to bar formation. We further aim to explore whether it is possible to classify simulated barred spiral galaxies based on the mechanism of their formation. We test the feasibility of this new classification schema with simulated galaxies. Using a set of 29,400 images obtained from our simulations, we first trained a convolutional neural network to distinguish between barred and non-barred galaxies. We then tested the network on simulations with different mass ratios and spin angles. We adapted the core neural network architecture for use with our additional aims. We find that a strong inverse relationship between mass ratio and the number of bars produced. We also identify two distinct phases in the bar formation process; (1) the initial, tidally induced formation pre-merger, and (2) the destruction and/or regeneration of the during and after the merger. Mergers with low mass ratios and closely-aligned orientations are considerably more conducive to bar formation compared to equal-mass mergers. We demonstrate the flexibility of our deep learning approach by showing it is feasible to classify bars based on their formation mechanism., Comment: 16 pages, 12 figures, accepted for publication in A&A