Galaxy Merger Rates up to z $\sim$ 3 using a Bayesian Deep Learning Model $-$ A Major-Merger classifier using IllustrisTNG Simulation data

@article{Ferreira2020GalaxyMR,
  title={Galaxy Merger Rates up to z \$\sim\$ 3 using a Bayesian Deep Learning Model \$-\$ A Major-Merger classifier using IllustrisTNG Simulation data},
  author={Leonardo F. Ferreira and Christopher J. Conselice and Kenneth Duncan and Ting-Yun Cheng and Alex Griffiths and Amy Whitney},
  journal={arXiv: Astrophysics of Galaxies},
  year={2020}
}
  • Leonardo F. Ferreira, Christopher J. Conselice, +3 authors Amy Whitney
  • Published 2020
  • Physics
  • arXiv: Astrophysics of Galaxies
  • Merging is potentially the dominate process in galaxy formation, yet there is still debate about its history over cosmic time. To address this we classify major mergers and measure galaxy merger rates up to z $\sim$ 3 in all five CANDELS fields (UDS, EGS, GOODS-S, GOODS-N, COSMOS) using deep learning convolutional neural networks (CNNs) trained with simulated galaxies from the IllustrisTNG cosmological simulation. The deep learning architecture used is objectively selected by a Bayesian… CONTINUE READING

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