Artificial intelligence, particularly machine learning, has become an invaluable tool for scientists across various disciplines, revolutionizing research and enabling groundbreaking discoveries.
New Findings Reveal Proto-Galaxy’s Structure and Formation Timeline
For two decades, astronomers have been searching for ancient “fossil” stars hidden among our galaxy’s bulge. These elusive stars predate the Milky Way and possess unique chemistry and orbits. Now, thanks to machine learning (AI) and the European Space Agency’s Gaia space telescope, researchers have uncovered a wealth of fossil stars and updated their understanding of our galaxy’s formation.
Artificial intelligence, particularly machine learning, has become an invaluable tool for scientists across various disciplines, revolutionizing research and enabling groundbreaking discoveries. For instance, AI systems have been employed to explore the permanently shadowed regions of the moon, providing invaluable data to identify suitable locations for future lunar missions based on surface properties.
Moreover, AI has played a pivotal role in astronomy, uncovering the clearest view of our galaxy to date and helping researchers uncover the origins of the Milky Way. In another remarkable feat, scientists have even leveraged AI to predict the properties of an entire universe, showcasing the immense potential of artificial intelligence in unlocking the secrets of the cosmos and propelling scientific advancements.
Debating Milky Way’s Origins
As revealed by Quanta Magazine, astronomers theorize that a proto-galaxy, a violent and chaotic region filled with young stars, preceded the Milky Way. Two prevailing formation theories emerged: either the proto-galaxy birthed the first stars internally from coalescing gas or it cannibalized other galaxies for stars and dark matter. To resolve this debate, researchers needed to identify the Milky Way’s earliest stars. To do this, they turned to the most up-to-date dataset available to them.
Gaia Data Unlocks Fossil Star Secrets
In 2022, Gaia’s third data release (DR3) provided detailed position measurements and low-resolution stellar spectra. Astrophysicist Vedant Chandra and his team used machine learning to extract heavy element signals from these spectra. By comparing their findings with high-quality sky surveys, the researchers identified around 18,000 early, low-metallicity stars in the Milky Way’s bulge.
Proto-Galaxy’s “Poor Old Heart”
Using Gaia’s DR3 velocity measurements, Chandra and his team uncovered each star’s orbit and determined that the proto-galaxy’s stars formed a halo-shaped structure with a 9,000 light-year radius. This “poor old heart” of the Milky Way suggests that the proto-galaxy did not steal stars from other galaxies, as their orbits would have extended beyond our own galaxy.
Birth of the Milky Way’s Disk
Chandra’s data set also confirmed a recent theory suggesting that after the proto-galaxy formed, it spent a billion years “simmering” and creating metal-poor stars before “boiling” and frantically producing metal-rich stars for 2-3 billion years. This process eventually led to the formation of the razor-thin disk of the Milky Way, filled with newer stars like our sun orbiting the galactic center. Researchers now plan to utilize the full 30-million-star data set to gain further insight into the Milky Way’s evolution.
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