Previously, astronomers had to manually sift through vast amounts of survey data to find these stars, followed by extensive observations to confirm their findings. Utilizing a new artificial intelligence method called manifold learning, a team led by University of Texas at Austin graduate student Malia Kao has drastically improved this process, achieving a 99% success rate in identification.

White dwarfs are stars in the final stage of their life cycle. After exhausting their fuel, they release their outer layers and gradually cool down. Our sun is expected to become a white dwarf in about 6 billion years.

Occasionally, planets orbiting a white dwarf are drawn in by the star's gravity, torn apart, and consumed. This process "pollutes" the star with heavy metals from the planet's interior. Since white dwarfs' atmospheres are primarily composed of hydrogen and helium, the presence of other elements indicates external contamination.

"For polluted white dwarfs, the inside of the planet is literally being seared onto the surface of the star for us to look at," said Kao. "Polluted white dwarfs right now are the best way we can characterize planetary interiors."

Keith Hawkins, an astronomer at UT and co-author of the paper, added, "It's the only bona fide way to actually figure out what planets outside the solar system are made of, which means finding these polluted white dwarfs is critical."

Detecting these stars is difficult because the evidence - polluting metals in their atmospheres - is subtle and fleeting. Despite being identifiable through manual data review, this method is laborious. To streamline the process, the team applied AI to data from the Gaia space telescope. "Gaia provides one of the largest spectroscopic surveys of white dwarfs to date, but the data is so low resolution that we thought it wouldn't be possible to find polluted white dwarfs with it," Hawkins said. "This work shows that you can."

Using manifold learning, the team developed an algorithm to analyze over 100,000 potential white dwarfs. One group of 375 stars exhibited the telltale signs of heavy metals in their atmospheres. Follow-up observations with the Hobby-Eberly Telescope at UT's McDonald Observatory confirmed their findings.

"Our method can increase the number of known polluted white dwarfs tenfold, allowing us to better study the diversity and geology of planets outside our solar system," said Kao. "Ultimately, we want to determine whether life can exist outside of our solar system. If ours is unique among planetary systems, it might also be unique in its ability to sustain life."

This innovative approach highlights how researchers at The University of Texas at Austin are using AI to tackle scientific challenges. In recognition of these advancements, UT Austin has declared 2024 the Year of AI.

This research utilized data from the European Space Agency (ESA) mission Gaia, processed by the Gaia Data Processing and Analysis Consortium. Follow-up observations were conducted using the Hobby-Eberly Telescope (HET), a collaboration between the University of Texas at Austin, the Pennsylvania State University, Ludwig Maximilians-Universitaet Muenchen, and Georg-August Universitaet Goettingen, as well as the Very Large Telescope (VLT) at the European Southern Observatory (ESO). The Texas Advanced Computing Center at UT Austin provided high-performance computing, visualization, and storage resources for this research.

Research Report:Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning

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