New Machine Learning Model Enhances View of the Milky Way Using Gaia Data
The Milky Way galaxy has always been a source of fascination for astronomers, but the sheer volume of data collected by missions like Gaia can be overwhelming. However, a team of scientists from the Leibniz Institute for Astrophysics Potsdam and the Institute of Cosmos Sciences of the University of Barcelona have found a way to efficiently process this data using advanced machine learning techniques.
By applying a novel machine learning model called SHBoost to the data from 217 million stars observed by the Gaia mission, the researchers were able to map properties such as interstellar extinction and metallicity across the Milky Way with unprecedented efficiency. This not only aids in understanding the stellar populations and galactic structure but also reduces the computational time, energy consumption, and CO2 emissions typically associated with such data processing.
Lead author of the study, Arman Khalatyan, explains that the SHBoost model can estimate key stellar properties like temperature, chemical composition, and interstellar dust obscuration in just four hours on a single GPU, a process that previously required two weeks and 3000 high-performance processors. This marks a significant advancement in the field of astronomy and sets a new standard for processing large datasets.
The model leverages high-quality spectroscopic data from smaller stellar surveys and applies it to Gaia’s extensive third data release, providing extensive maps of the Milky Way’s overall chemical composition and shedding light on the distribution of young and old stars. The data also highlights areas with few young stars, known as “stellar voids,” and points out gaps in our understanding of the three-dimensional distribution of interstellar dust.
As Gaia continues to gather data, machine-learning models like SHBoost are becoming essential tools for astronomers to quickly and sustainably process large datasets. This success not only revolutionizes data analysis in astronomy but also paves the way for more sustainable research practices in the future.
