First discovery of machine learning in astronomy

Machine Learning

Advances in telescope technology are giving scientists unprecedented amounts of data, necessitating new ways of analyzing it. Machine learning has emerged as a valuable tool for astronomers to quickly and accurately sift through the vast amount of information produced by new telescopes, including the MeerKAT radio telescope in South Africa. Using a coding framework called Astronomaly, the researcher was able to identify a previously overlooked object, SAURON, in his MeerKAT cluster legacy survey. Although there are similarities to the Odd Radio Circle, which was first discovered in 2019, Sauron has unique features that suggest he may be the result of a merger of two supermassive black holes. Read more below.

Astronomers used machine learning to mine data from South Africa’s MeerKAT telescope: What they found

Michelle Lockner

New telescopes with unprecedented sensitivity and resolution are being announced around the world. Among them are the Giant Magellan Telescope under construction in Chile and the James Webb Space Telescope located 1.5 million km away from him in space.

This means there is a wealth of data available to scientists. The raw data from just one of his observations from the MeerKAT radio telescope in South Africa’s Northern Cape Province can measure 1 terabyte. This is enough to fill up the hard drive of a laptop computer. MeerKAT is an array of 64 large antenna dishes. It uses radio signals from space to study the evolution of the universe and everything it contains, including galaxies. Each dish is said to generate as much data as a DVD in one second.

Machine learning can help astronomers process this data faster and more accurately than manually examining it. Perhaps surprisingly, despite our increasing reliance on computers, until recently rare and new discoveries in astrophysical phenomena relied entirely on human inspection of data.

Machine learning is basically a set of algorithms designed to automatically learn patterns and models from data. We astronomers don’t know what we’re trying to find, so we also design algorithms to look for anomalies that don’t fit known parameters or “labels.”

This approach allowed my colleagues and I to find previously overlooked objects in the MeerKAT data. It is about 7 billion light years away from Earth (1 light year is a measure of the distance light travels in her one year). From what we know so far about this object, it has many components of what is known as the Odd Radio Circle (ORC).

Odd radio circles can be identified by their strange ring-like structure. Only a handful of these circles have been detected since their initial discovery in 2019, so they are still largely unknown.

A new paper outlines the features of a potential odd radio circle, named SAURON (Steep and Nonuniform Ring of Non-Thermal Radiation). SAURON is, to our knowledge, the first scientific discovery made on his MeerKAT data using machine learning. (Several other discoveries were made with the help of machine learning in astronomy.)

Not only is discovering something new incredibly exciting, new discoveries are important because they challenge our understanding of the universe. may be consistent with our theory. Or maybe we need to change the way we see the universe. New discoveries of unusual astrophysical objects help advance science.

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Anomaly identification

I discovered SAURON in the data of the MeerKAT Galaxy Cluster Legacy Survey. The survey is an observational program conducted at his MeerKAT telescope in South Africa, the predecessor of the Square Kilometer Array. The array is a global project to build the world’s largest and most sensitive radio telescopes in South Africa and Australia within the next decade.

This survey was conducted between June 2018 and June 2019. We aimed at about 115 galaxy clusters, each consisting of hundreds or thousands of galaxies.

This is a lot of data to sift through, and that’s where machine learning comes in.

We developed and used a coding framework called Astronomary to classify our data. Astronomers ranked the unknown objects according to an anomaly scoring system. A team of humans then manually evaluated the 200 anomalies of most interest. We’ve tapped into our vast collective expertise to make sense of the data.

It was in this part of the process that we identified SAURON. Instead of having to look at 6,000 individual images, just look at the first 60 of his that astronomy flagged as an anomaly to detect SAURON.

But questions remain. What did you find, exactly?

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Is Sauron an odd radio circle?

We know very little about Odd Radio Circles. These bright explosion-like emissions are now thought to be the remnants of a gigantic explosion in the host galaxy.

Sauron.Michelle Lockner

The name SAURON captures the basics of object composition. “Steep” refers to the slope of its spectrum, indicating that the “source” (or object) fades very quickly at higher radio frequencies. “Ring” refers to the shape. Also, “non-thermal radiation” refers to a type of radiation, suggesting that there must be particles that accelerate in strong magnetic fields. Sauron is at least 1.2 million light-years across, about 20 times the size of the Milky Way.

But SAURON doesn’t tick all the right boxes to be arguably Odd Radio Circle. We detected a host galaxy, but found no evidence of radio emissions at wavelengths and frequencies consistent with those of other known ORC host galaxies.

SAURON also has much in common with the first discovered Odd Radio Circle, Odd Radio Circle1, but is otherwise different. Its strange shape and strangely behaving magnetic field do not mesh well with the main structure.

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One of the most exciting possibilities is that Sauron is the remnant of an explosive merger of two supermassive black holes. These are incredibly dense objects in the centers of galaxies like the Milky Way, which can cause massive explosions when galaxies collide.

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