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AI Trained to Recognize Deep-Space Galaxies



Researchers repurposed a Facebook AI that recognizes people in photos to identify galaxies in deep space.

The new bot, named ClaRAN, scans radio telescope images in hopes of spotting radio galaxies, which emit powerful radio jets from a supermassive black holes (SMBH).

ClaRan is the brainchild of big data specialist. Wu and astronomer Ivy Wong, both from the University of Western Australia node of the International Center for Radio Astronomy Research (ICRAR).

Supermassive black holes exist at the center of almost all currently known massive galaxies. In the case of our Milky Way, the SMBH with the location of Sagittarius A *.

According to Wong, these black holes occasionally "burp out" jets that can be seen with a radio telescope.

"Over time "The Jets Can Stretch Away From Their Hosts Galaxy, Making It Difficult For Traditional Computer Programs to figure out where the galaxy is," she explained. ClaRAN is so.

ClaRAN is so. "

ClaRAN is so GitHub.

By combining the data from different telescopes, ClaRAN's confidence level in its detections and classifications is increased. Via the Wu & Ivy Wong / ICRAR / UWA)

The ClaRAN is extremely confident that the source is a radio galaxy jet system. 70 million with the upcoming Evolutionary Map of the Universe (EMU) survey.

Australian Square Kilometer Pathfinder (ASKAP ) telescope to make a census of radio sources in the sky. Traditional computer algorithms should be 90 percent of those sources.

"That still leaves 10 percent, or 7 million 'difficult' galaxies that have been subject to a human due to the complexity of their extended structures," Wong said.

"If ClaRAN reduces the number of sources that require visual classification down to 1 percent, this means more time for our citizen scientists to spend on new types of galaxies."

Wong previously harnessed Radio Galaxy zoo project.

Volunteers from the group helped produce the catalog ClaRan-an example of a new paradigm Wu called "programming 2.0."

"All you "It's a set-up-a-huge-network, give it a ton of data, and let it figure out how to adjust its internal connections."

A research paper on ClaRAN Monthly Notices of the Royal Astronomical Society published by Oxford University Press.

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