Astronomers have recently found hundreds of "polluted" white dwarf stars in our home galaxy, the Milky Way. These are white dwarfs caught actively consuming planets in their orbit. They are a valuable resource for studying the interiors of these distant, demolished planets. They are also difficult to find. Historically, astronomers have had to manually review mountains of survey data for signs of these stars. Follow-up observations would then prove or refute their suspicions.

In a study published in the Astrophysics Journal, a team led by University of Texas at Austin graduate student Malia Kao accelerated the process by using a novel form of artificial intelligence called manifold learning, leading to a 99% success rate in identification.

What are white Dwarfs?

White dwarfs are stars in their final stage of life. They've used their fuel, released their outer layers into space and are slowly cooling. One day, our sun will become a white dwarf—but that won't be for another 6 billion years.

Sometimes, the planets orbiting a white dwarf are drawn in by their star's gravity, ripped apart, and consumed. When this happens, the star becomes "polluted" with heavy metals from the planet's interior. Because white dwarfs' atmospheres are made almost entirely of hydrogen and helium, the presence of other elements can be reliably attributed to external sources.

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

Leveraging AI

Although astronomers can identify these stars by manually reviewing data from astronomical surveys, this can be time-intensive. To test out a faster process, the team applied AI to data available from the Gaia space telescope.

Keith Hawkins, an astronomer at UT and co-author of the paper, remarked that Gaia provides one of the largest spectroscopic surveys of white dwarfs to date. Still, the data is so low resolution that they thought it impossible to find polluted white dwarfs with it.

The team used the AI technique called manifold learning to find these elusive stars. With it, an algorithm looks for similar features in a data set and clumps like items together in a simplified visual chart. Researchers can then review the chart and decide what clumps warrant further investigation.

The astronomers created an algorithm to sort over 100,000 possible white dwarfs. Of these, one clump of 375 stars looked promising: They showed the key feature of having heavy metals in their atmospheres. Follow-up observations with the Hobby-Eberly Telescope at UT's McDonald Observatory confirmed the astronomers' suspicions.

Kao believes that their method can tenfold the number of known polluted white dwarfs, allowing the researcher to better study the diversity and geology of planets outside our solar system. Ultimately, they 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.

Phys Org