A Simulation-driven Deep Learning Approach for Separating Mergers and Star-forming Galaxies: The Formation Histories of Clumpy Galaxies in All of the CANDELS Fields

Being able to distinguish between galaxies that have recently undergone major merger events, or are experiencing intense star formation, is crucial for making progress in our understanding of the formation and evolution of galaxies. As such, we have developed a machine learning framework based on a convolutional neural network (CNN) to separate star forming galaxies from post-mergers using a dataset of 160,000 simulated images from IllustrisTNG100 that resemble observed deep imaging of galaxies with Hubble. We improve upon previous methods of machine learning with imaging by developing a new approach to deal with the complexities of contamination from neighbouring sources in crowded fields and define a quality control limit based on overlapping sources and background flux. Our pipeline successfully separates post-mergers from star forming galaxies in IllustrisTNG $80\%$ of the time, which is an improvement by at least 25\% in comparison to a classification using the asymmetry ($A$) of the galaxy. Compared with measured S\'ersic profiles, we show that star forming galaxies in the CANDELS fields are predominantly disc-dominated systems while post-mergers show distributions of transitioning discs to bulge-dominated galaxies. With these new measurements, we trace the rate of post-mergers among asymmetric galaxies in the universe finding an increase from $20\%$ at $z=0.5$ to $50\%$ at $z=2$. Additionally, we do not find strong evidence that the scattering above the Star Forming Main Sequence (SFMS) can be attributed to major post-mergers. Finally, we use our new approach to update our previous measurements of galaxy merger rates $\mathcal{R} = 0.022 \pm 0.006 \times (1+z)^{2.71\pm0.31}$Comment: Accepted for publication in ApJ. 28 pages, 18 Figures, 2 Tables. Pretrained models available at https://github.com/astroferreira/FERREIRA202

The JWST Hubble Sequence: The Rest-frame Optical Evolution of Galaxy Structure at 1.5 < z < 6.5

© 2023. The Author(s). Published by the American Astronomical Society. This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, see: https://creativecommons.org/licenses/by/4.0/We present results on the morphological and structural evolution of a total of 3956 galaxies observed with JWST at 1.5 109 M ⊙ at z > 3 are not dominated by irregular and peculiar structures, either visually or quantitatively, as previously thought. We find a strong dominance of morphologically selected disk galaxies up to z = 6 in this mass range. We also find that the stellar mass and star formation rate densities are dominated by disk galaxies up to z ∼ 6, demonstrating that most stars in the Universe were likely formed in a disk galaxy. We compare our results to theory to show that the fraction of types we find is predicted by cosmological simulations, and that the Hubble Sequence was already in place as early as one billion years after the Big Bang. Additionally, we make our visual classifications public for the community.Peer reviewe

The JWST hubble sequence: the rest-frame optical evolution of galaxy structure at 1.5< z <6.5

We present results on the morphological and structural evolution of a total of 3956 galaxies observed with JWST at 1.5 109 M ⊙ at z > 3 are not dominated by irregular and peculiar structures, either visually or quantitatively, as previously thought. We find a strong dominance of morphologically selected disk galaxies up to z = 6 in this mass range. We also find that the stellar mass and star formation rate densities are dominated by disk galaxies up to z ∼ 6, demonstrating that most stars in the Universe were likely formed in a disk galaxy. We compare our results to theory to show that the fraction of types we find is predicted by cosmological simulations, and that the Hubble Sequence was already in place as early as one billion years after the Big Bang. Additionally, we make our visual classifications public for the community.</p