Astronomers discover super-bright quasar lenses

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Astronomers discover super-bright quasar lenses

AI is a powerful tool for big data discoveries, researchers say

An international team of scientists has used machine learning to identify seven rare quasar candidates, according to a new study.

Quasars, distant cores of galaxies powered by supermassive black holes, are among the most luminous objects in the universe. While not uncommon, their brightness can make it difficult to accurately measure the galaxy they reside in. This means scientists must use gravitational lensing to assist in analyzing these bright objects, a method that relies on studying how an object's strong gravity bends light around its host galaxy. Yet despite their own powerful gravity, finding quasars that can act as lenses is uncommon.

Moreover, while nearly every galaxy is home to a black hole, research suggests those that form quasars may act as "missing links" into the formation and evolution of the early universe. Young ones, especially, could be key to unlocking vast cosmic secrets.

Now, to identify more quasars as gravitational lenses, researchers analyzed a list of 800,000 quasars from the Dark Energy Spectroscopic Instrument (DESI) survey. Then, using an AI model trained on a small sample of mock lenses, or fake examples of quasar lens systems, to automatically search for these rare events, researchers found seven new candidates.

"Quasars are like the baby pictures of a supermassive black hole," said Everett McArthur, lead author of the study and a graduate student in astronomy at The Ohio State University. "So exploring how we get from quasars to those black holes is really important."

These new candidates double the number of quasars scientists have found by surveys in years past, and with more data, the discovery offers an opportunity to expand our knowledge of how their systems work as well as how the galaxy they reside in grows and evolves.

For instance, while the seven candidates in this study are located at least 5 to 6 billion light-years away from Earth, uncovering new insights about these faraway objects could also reveal valuable information about our own galaxy, said McArthur.

"By studying the tight correlation between galaxies and black holes, we could understand why our galaxy is the way that it is and perhaps why our own black hole is sometimes dormant," he said.

The study was published July 22 in The Astrophysical Journal.

Outside of the team's observations, what is unique about its work is the use of neural networks to achieve its result. Since there aren't enough real-life examples of quasars acting as lenses, researchers had to teach their AI to identify the emission lines of potential quasars as gravitational lenses using a mixture of real quasar and background galaxy spectra.

This method created a simulation so impressive that the AI was able to recognize the subtle differences between normal and abnormal quasars with unique features, said McArthur.

"What this proves is our architecture was able to parse through a diverse array of quasar spectra in a really significant way," said McArthur.

After whittling DESI's list of 800,000 potential quasars to 200, the team hand-reviewed the shortened list before narrowing down the candidates to a final seven.

Going forward, the researchers will seek to directly confirm their observations using powerful space-based instruments like the Hubble Telescope. Once those deeper studies are completed, with more data, they expect to use their AI model to help future scientists search for and validate other kinds of strange cosmic phenomena.

"You can very well expand this type of study to find many rare anomalies in a spectrum," said McArthur. "We're in an era when science has suddenly become more accessible than ever, and applying AI to astronomy and machine learning methods to big data sets is part of that."

Other co-authors from Ohio State are Klaus Honscheid and Claire Lamman. This work was supported by the U.S. Department of Energy and the European Union's Horizon 2020 Research and Innovation program.

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