AI Space Odyssey: Deep Learning Aids Astronomers Study Galaxies
The Milky Way is on a collision course with the neighboring Andromeda galaxy. But no need to revise your will — the two star systems won’t meet for around 4 billion years.
“At some point in every galaxy’s life, it’ll undergo one of these mergers,” said William Pearson, Ph.D. student at the Netherlands Institute for Space Research and the University of Groningen, Netherlands. “It’s part of our understanding of how we think the universe works. These galaxies tend to find and crash into each other.”
When two galaxies merge, the resulting fused galaxy mixes together all the gas, dust and other matter from the original star systems. Astronomers are interested in how the shape of galaxies change as a result, how the process can cause stars to form at a higher rate, and how the moving matter interacts with the supermassive black holes lying at the center of large galaxies.
By using AI to identify and analyze galaxy mergers across the universe, scientists can better understand how this phenomenon could affect our corner of the universe in the future.
Hubble Up: Analyzing Galaxy Mergers with AI
For the most part, it’s not rocket science to visually determine whether two galaxies are in the thick of a collision.
Just looking at a telescope image, it’s easy to spot tidal tails, sweeping arcs of gas and dust being pulled from one galaxy to another by gravity.
The main challenge is classifying galaxies that are just starting to interact, or, on the other end of the spectrum, at the very final stages of a merge.
And then there’s the sheer volume of data.
Crowdsourced projects like Galaxy Zoo have relied on citizen scientists to classify a database of more than a million galaxy images from various ground-based and satellite telescopes. But that’s just a fraction of an estimated 100 billion galaxies in the universe.
And the available data is just getting larger. Projects like the under-construction Large Synoptic Survey Telescope are expected to capture images of billions of galaxies.
“There’s not enough people in the world to classify all these,” Pearson said. “As astronomers, we need another technique.”
While citizen scientist projects are a powerful tool, it still takes a long time for results to come through, he says. Deep learning models can help researchers keep pace with the many ground- and space-based telescopes busy collecting images of the universe, most of which are publicly available for analysis.
Using an NVIDIA GPU for inference, Pearson’s AI was able to categorize 300,000 galaxies in about 15 minutes. Even at an unheard-of rate of one classification per second, it would have taken an individual two working weeks to accomplish the task.
Trained using the TensorFlow deep learning framework and images from the Sloan Digital Sky Survey, the deep learning model identifies galaxies as merging or not merging with 92 percent accuracy. Pearson hopes for future versions of the CNN to look at more specific details, such as the size of the galaxies and how far along the merging process is.
From this data, researchers can make statistical assessments of broad trends in galaxy mergers — or take a closer look at specific galaxies of interest.
Main image shows two merging galaxies, nicknamed “The Mice,” located 300 million light-years away. Image credit: NASA, Holland Ford (JHU), the ACS Science Team and ESA. Licensed under CC BY 4.0.
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