In the News
Synthetic Training Data Used to Find Galaxies in Different Stages of Formation
In this study, accepted for publication in Astrophysical Journal and available online, researchers used computer simulations of galaxy formation to create synthetic training data for a deep learning algorithm, which then proved surprisingly good at analyzing images of galaxies from the Hubble Space Telescope.
In the image shown, the top row are high resolution renderings from a state-of-the-art galaxy simulation program (VELA). The second row are the same images from row one but modified to more closely resemble what would be observed by the Hubble optical systems; these images were used to train the neural network. The bottom row are Hubble galaxy photos correctly classified using the deep learning system developed by the researchers.
“We were not expecting it to be all that successful. I’m amazed at how powerful this is,” said coauthor Joel Primack, professor emeritus of physics and a member of the Santa Cruz Institute for Particle Physics (SCIPP) at UC Santa Cruz. “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.”
Read the entire post by author Tim Stephens here.
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