Journal
ASTROPHYSICAL JOURNAL
Volume 931, Issue 1, Pages -Publisher
IOP Publishing Ltd
DOI: 10.3847/1538-4357/ac66ea
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Funding
- Coordernacao de Aperfeicoamento de Pessoal de Nivel Superior-Brazil (CAPES)
- European Research Council (ERC) Advanced Investigator grant EPOCHS [788113]
- Science and Technology Facilities Council [RA27PN]
- European Research Council (ERC) [788113] Funding Source: European Research Council (ERC)
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In this study, a machine-learning framework based on a convolutional neural network is developed to distinguish between star-forming galaxies and post-mergers. The framework successfully separates these galaxies with higher accuracy compared to previous methods, and the new measurements provide insights into the formation and evolution of galaxies.
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 postmergers 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 R = 0.022 +/- 0.006 x (1 + z )(2.)(71)(+/- 0.)(31).
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