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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

Ferreira, Leonardo; Conselice, Christopher J.; Kuchner, Ulrike; Tohill, Clár-Bríd

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 Thumbnail


Authors

Leonardo Ferreira

Christopher J. Conselice

Clár-Bríd Tohill



Abstract

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 to separate star-forming galaxies from post-mergers using a data set 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 neighboring 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 Sersic profiles, we show that star-forming galaxies in the CANDELS fields are predominantly disk-dominated systems while post-mergers show distributions of transitioning disks 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 can be attributed to major post-mergers. Finally, we use our new approach to update our previous measurements of galaxy merger rates 3/4=0.022±0.006×(1+z)2.71±0.31 .

Journal Article Type Article
Acceptance Date Apr 11, 2022
Online Publication Date May 23, 2022
Publication Date May 1, 2022
Deposit Date Dec 22, 2022
Publicly Available Date Dec 22, 2022
Journal Astrophysical Journal
Print ISSN 0004-637X
Electronic ISSN 1538-4357
Publisher American Astronomical Society
Peer Reviewed Peer Reviewed
Volume 931
Issue 1
Article Number 34
DOI https://doi.org/10.3847/1538-4357/ac66ea
Keywords Space and Planetary Science; Astronomy and Astrophysics
Public URL https://nottingham-repository.worktribe.com/output/15168227
Publisher URL https://iopscience.iop.org/article/10.3847/1538-4357/ac66ea

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