Aging Stars with Confidence: How Neural Networks and Uncertainty Help Date the Cosmos
This study introduces a Bayesian neural network model to estimate stellar ages using chemical abundance data. By modeling uncertainties directly, the approach yields accurate and cautious age predictions for main sequence stars, achieving errors under 1 billion years. It rivals traditional methods while offering flexibility and improved uncertainty handling, making it valuable for broader stellar and galactic studies.
Exploring the History of the Milky Way with Gaia’s Giant Stars
The study uses Gaia data and machine learning models to estimate the ages of giant stars, revealing insights into the Milky Way's evolution. By analyzing over 2.2 million stars, the researchers identified three major phases in the galaxy's history, including a starburst triggered by a major merger and the formation of the thin disc. Their method advances our ability to trace the Milky Way's structure and development.