Astronomers using the Hobby-Eberly Telescope at McDonald Observatory in Fort Davis have recently confirmed the discovery of hundreds of “polluted” white dwarf stars in the Milky Way galaxy. These stars are in the process of consuming orbiting planets, providing a valuable resource for studying the interiors of these distant, demolished planets. This breakthrough was made possible by artificial intelligence (AI) and represents a significant advancement in the field.
Historically, identifying these polluted white dwarfs required manual analysis of vast amounts of survey data, followed by observational verification. However, a novel AI technique known as manifold learning has dramatically expedited this process. The method, developed by a team led by University of Texas at Austin graduate student Malia Kao, has achieved a 99% success rate in identifying these elusive stars.
White dwarfs are the final evolutionary stage of stars like our Sun, which will become a white dwarf in about 6 billion years. When a white dwarf’s gravity pulls in and disintegrates an orbiting planet, the star becomes polluted with heavy metals from the planet’s interior. These metals, detectable in the white dwarf’s atmosphere, provide a unique opportunity to study the composition of distant planets.
“For polluted white dwarfs, the inside of the planet is literally being seared onto the surface of the star for us to look at,” explained Kao. “Polluted white dwarfs right now are the best way we can characterize planetary interiors.”
Keith Hawkins, an astronomer at UT Austin and co-author of the study, emphasized the importance of this discovery: “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.”
The challenge lies in the subtle and transient nature of the polluting metals’ evidence in white dwarf atmospheres. To overcome this, the team applied AI to data from the Gaia space telescope, using manifold learning to identify potential polluted white dwarfs. The AI sorted over 100,000 possible white dwarfs, identifying a promising group of 375 stars showing signs of heavy metals.
Follow-up observations with the Hobby-Eberly Telescope at McDonald Observatory confirmed these 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.
This research, published in the Astrophysical Journal, showcases how UT Austin is leveraging AI to address scientific challenges. The project utilized data from the European Space Agency’s Gaia mission and involved follow-up observations with both the Hobby-Eberly Telescope and the Very Large Telescope at the European Southern Observatory.
By significantly increasing the number of known polluted white dwarfs, this innovative approach offers greater insight into the composition and distribution of planets within our galaxy, ultimately aiding in the quest to determine whether life can exist beyond our solar system.
