Unraveling the History of the Milky Way

Abstract

Understanding physical processes responsible for the formation and evolution of galaxies like the Milky Way is a fundamental problem in astrophysics. However, a key challenge is that the properties and orbits of the stars can only be observed at present. In order to understand what happened in the Milky Way at earlier epochs, one must explore "archaeological'" techniques. One idea, "chemical tagging,"' aims to probe the history of the Milky Way via the unique imprint in elemental abundances of long-disrupted star clusters even the stars are now on widely dispersed orbits and spatially mixed. In my thesis, I developed the first extensive model of the Milky Way in the context of chemical tagging and explored the opportunities and challenges associated with chemical tagging. My work put the first constraint on the disrupted cluster mass function in the Milky Way and revealed that the Milky Way did not form very massive star clusters in the first five billion years. As part of this effort, I also developed a new technique for measuring stellar properties from large spectroscopic surveys, adopting ideas in machine learning. My technique provides new opportunities for estimating stellar properties more precisely and exploiting the information embedded in low-resolution spectra. This technique has many promises and might hold the key to realizing the full potential of chemical tagging in the future.