Chemical tagging and age estimation with the GALAH DR4 survey

Abstract

Stellar chemical abundances encode valuable information about the formation and evolution of the Milky Way. In this work, we explore two complementary approaches to extract this information from the GALAH DR4 survey. First, we explore the use of a supervised machinelearning algorithm to estimate stellar ages for red giant stars from their chemical abundances and atmospheric parameters, using asteroseismic ages as training data. While the model is able to recover a global age trend, the predicted ages show an unexpectedly poor precision. Second, we analyse the multidimensional chemical abundance space of red clump stars using an unsupervised clustering method, identifying chemically coherent groups. These groups display distinct chemical patterns that can be associated with different components of the Galactic disc.