Exploring the History of the Milky Way with Gaia’s Giant Stars
In astronomy, understanding how the Milky Way formed is like piecing together a cosmic puzzle. A key part of this puzzle is finding the ages of stars. Unlike their temperature or brightness, a star’s age cannot be directly measured. Scientists rely on patterns in stars’ light and chemistry to estimate their ages. This paper, led by Aisha S. Almannaei, explores a new way to determine the ages of giant stars using data from the Gaia spacecraft. The researchers developed machine learning models that make it easier to study the history of the Milky Way, especially its chemical and structural evolution.
Why Giant Stars?
Giant stars are valuable because they are very bright and can be seen across large distances in the galaxy. However, estimating their ages is tricky because their light patterns often overlap, making traditional methods less effective. Previous studies showed that the chemical elements in a star, like carbon and nitrogen, could reveal its age. Combining this idea with advanced data from Gaia, the researchers trained two models—SIDRA-RVS and SIDRA-XP—to predict star ages more precisely.
The Tools: Gaia Data and Machine Learning
Gaia collects two types of data used in this study: high-resolution spectra (RVS) and low-resolution spectra (XP). Spectra reveal the light a star emits at different wavelengths, which contains clues about its temperature, size, and chemical composition. By feeding these features into their models, the team trained them to predict the ages of stars. They used a reference set of star ages derived from earlier studies that combined detailed chemistry and vibrations within stars, a method called asteroseismology.
Model Results: SIDRA-RVS and SIDRA-XP
The first model, SIDRA-RVS, used high-resolution spectra to predict ages and showed solid accuracy, particularly for older stars. However, SIDRA-XP, which relied on low-resolution spectra and derived chemical properties like carbon and nitrogen levels, performed even better. The team found that combining a star's chemistry with basic physical properties like temperature and surface gravity significantly improved age predictions.
Mapping the Milky Way’s History
Using the SIDRA-XP model, the researchers analyzed over 2.2 million stars. They uncovered three major phases in the Milky Way’s evolution:
Early Formation (over 12 billion years ago): Stars were metal poor, reflecting a time before heavy elements were common in the galaxy.
A Galactic Starburst (9–12 billion years ago): A merger with another galaxy, Gaia-Sausage-Enceladus, triggered rapid star formation and chemical enrichment.
The Thin Disc (under 9 billion years): The galaxy settled into a calmer phase, forming younger, metal-rich stars.
They also found signs of a second merger event about 7 billion years ago, possibly involving the Sagittarius dwarf galaxy.
Why It Matters
This study demonstrates how machine learning can unlock new insights from Gaia’s data, offering a powerful way to trace the Milky Way’s evolution. By understanding the ages and chemistry of giant stars, scientists can piece together the galaxy’s history, including major events like mergers and starbursts.
Conclusion: A Bright Future for Galactic Archaeology
The work of Almannaei and her team highlights how combining advanced data from space missions with machine learning can deepen our understanding of the cosmos. With future Gaia data releases, this method could provide even more detailed maps of the Milky Way’s structure and its dynamic past.
Source: Almannaei
