Astronomers report success with machine deep learning

Astronomers report success with machine deep learning

Machine learning continues its successes in astronomy.

Classifying galaxies. On April 23, 2018, astronomers at UC Santa Cruz reported using machine deep learning techniques to analyze images of galaxies, with the goal of understanding how galaxies form and evolve. This new study has been accepted for publication in the peer-reviewed Astrophysical Journal and is available online. In the study, researchers used computer simulations of galaxy formation to train a deep learning algorithm, which then:

… proved surprisingly good at analyzing images of galaxies from the Hubble Space Telescope.

The researchers said they used output from their simulations to generate mock images of simulated galaxies as they would look in ordinary Hubble observations. The mock images were used to train the deep learning system to recognize three key phases of galaxy evolution. The researchers then gave their artificial neutral network a large set of actual Hubble images to classify.
The results showed a remarkable level of consistency, the astronomers said, in the classifications of simulated and real galaxies. Joel Primack of UC Santa Cruz said:

We were not expecting it to be all that successful. I’m amazed at how powerful this is. We know the simulations have limitations, so we don’t want to make too strong a claim. But we don’t think this is just a lucky fluke.

Jane Merdith, Tendron Systems Ltd, London, UK.