KEEP IN TOUCH
/AllAboutSpaceMagazine
@spaceanswers
space@spaceanswers.com
A new machine-learning algorithm aims to provide improved measurements of stellar ages, allowing astronomers to better model how stars evolve. The algorithm is an AI version of a project called EAGLES, which stands for Estimating Ages from Lithium Equivalent Widths. EAGLES uses the lithium abundance of stars to determine their age. Previously this work had been done by fitting data to graphs. With surveys producing more and more data, this task has grown time-consuming and complex, so an AI has been written to take on the job.
All stars are born containing the same proportion of lithium, but as they age they lose this lithium at different rates depending on their masses, and therefore temperatures – the more massive the star, the hotter the temperature, which astronomers use as a proxy because they can’t measure the mass of the star directly. The hotter a star, the greater the rate of convection in that star’s outer layers and the more this churns up the lithium on a star’s surface. As lithium sinks into a star’s interior, it’s converted into two helium nuclei by fusing with a proton. The result is that the lithium is increasingly depleted as time goes by. Therefore, the abundance of lithium observed in a star, coupled with the star’s temperature, should together provide a measure of that star’s age.
Traditionally, astronomers measure a star’s age with lithium by looking at the strength of the lithium spectral line in a star’s spectrum – which is what ‘equivalent widths’ in EAGLES’ name refers to – then trying to fit it to models of stellar evolution. Not only is this method “difficult to do and requires a lot of work,” but scientists also want to expand beyond lithium abundances to include other stellar properties that can indicate age as well, said George Weaver of Keele University. Weaver and his supervisor, Robin Jeffries, have thus introduced AI to take on some of the workload, particularly when handling lots of information covering the other age indicators coming in from big all-sky surveys. It’s possible AI can find previously undiscovered relationships in stars’ data.
Astronomers can more easily measure the relative ages of stars in star clusters because a cluster’s stars were all born at the same time, meaning they can be directly compared based on how they have evolved. Weaver and Jeffries sampled 6,000 stars from a total of 52 clusters observed by the European Space Agency’s Gaia mission. Then they trained the EAGLES algorithm on the selected stellar bodies. “A stellar evolution model tells you what a star should look like as a function of age,” Jeffries said. “If we have stars whose ages we know, that’s very helpful wh
