DeepGalaxy: Deducing the Properties of Galaxy Mergers from Images Using
  Deep Neural Networks
  Galaxy mergers, the dynamical process during which two galaxies collide, are
among the most spectacular phenomena in the Universe. During this process, the
two colliding galaxies are tidally disrupted, producing significant visual
features that evolve as a function of time. These visual features contain
valuable clues for deducing the physical properties of the galaxy mergers. In
this work, we propose DeepGalaxy, a visual analysis framework trained to
predict the physical properties of galaxy mergers based on their morphology.
Based on an encoder-decoder architecture, DeepGalaxy encodes the input images
to a compressed latent space $z$, and determines the similarity of images
according to the latent-space distance. DeepGalaxy consists of a fully
convolutional autoencoder (FCAE) which generates activation maps at its 3D
latent-space, and a variational autoencoder (VAE) which compresses the
activation maps into a 1D vector, and a classifier that generates labels from
the activation maps. The backbone of the FCAE can be fully customized according
to the complexity of the images. DeepGalaxy demonstrates excellent scaling
performance on parallel machines. On the Endeavour supercomputer, the scaling
efficiency exceeds 0.93 when trained on 128 workers, and it maintains above
0.73 when trained with 512 workers. Without having to carry out expensive
numerical simulations, DeepGalaxy makes inferences of the physical properties
of galaxy mergers directly from images, and thereby achieves a speedup factor
of $\sim 10^5$.
