@article { , title = {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}, 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 .}, doi = {10.3847/1538-4357/ac66ea}, eissn = {1538-4357}, issn = {0004-637X}, issue = {1}, journal = {Astrophysical Journal}, publicationstatus = {Published}, publisher = {American Astronomical Society}, url = {https://nottingham-repository.worktribe.com/output/15168227}, volume = {931}, keyword = {Space and Planetary Science, Astronomy and Astrophysics}, year = {2022}, author = {Ferreira, Leonardo and Conselice, Christopher J. and Kuchner, Ulrike and Tohill, Clár-Bríd} }