
Nicole Buckley
About
My research project
Delving into the Accretion TImeline of the Milky WayThe Milky Way is enshrouded in a dark matter halo containing scattered debris of infalling smaller systems of stars called dwarf galaxies and globular clusters. Even though these stars are spread out within the halo, their ‘chemical fingerprint’ remains intact. This fingerprint is a unique signature of the properties of these systems – their size and how quickly stars formed within. The first goal of this PhD is to develop a state-of-the-art machine-learning algorithm that can disentangle the chemical fingerprints in the halo, thus revealing the systems that have been engulfed by our Milky Way. We will collaborate with researchers developing simulations of the evolution of galaxies to test our new algorithm. The second goal is to patch together surveys measuring the chemical fingerprints of stars extending from the inner to the outer halo. This will enable us to recover the full mass spectrum of infalling stellar systems as the largest are able to penetrate the inner halo, while the smallest start shedding stars in the outer halo. Some of the stars can be dated using precise estimates that analyse their pulsations, enabling us to map out the formation timeline of the Milky Way. The final goal is to reveal the histories of these ‘accreted’ dwarf galaxies and globular clusters, by modelling how chemical elements are produced in their stars and dispersed into the surrounding gas, producing the next generation of stars. Thus, allowing us to shed light on the formation of some of the smallest stellar objects in our Universe.
Supervisors
The Milky Way is enshrouded in a dark matter halo containing scattered debris of infalling smaller systems of stars called dwarf galaxies and globular clusters. Even though these stars are spread out within the halo, their ‘chemical fingerprint’ remains intact. This fingerprint is a unique signature of the properties of these systems – their size and how quickly stars formed within. The first goal of this PhD is to develop a state-of-the-art machine-learning algorithm that can disentangle the chemical fingerprints in the halo, thus revealing the systems that have been engulfed by our Milky Way. We will collaborate with researchers developing simulations of the evolution of galaxies to test our new algorithm. The second goal is to patch together surveys measuring the chemical fingerprints of stars extending from the inner to the outer halo. This will enable us to recover the full mass spectrum of infalling stellar systems as the largest are able to penetrate the inner halo, while the smallest start shedding stars in the outer halo. Some of the stars can be dated using precise estimates that analyse their pulsations, enabling us to map out the formation timeline of the Milky Way. The final goal is to reveal the histories of these ‘accreted’ dwarf galaxies and globular clusters, by modelling how chemical elements are produced in their stars and dispersed into the surrounding gas, producing the next generation of stars. Thus, allowing us to shed light on the formation of some of the smallest stellar objects in our Universe.
Publications
ABSTRACT We present a detailed study of the chemical diversity of the metal-poor Milky Way using data from the GALAH DR3 survey. Considering 17 chemical abundances relative to iron ([X/Fe]) for 9923 stars, we employ principal component analysis (PCA) and extreme deconvolution (XD) to identify 10 distinct stellar groups. This approach, free from chemical or dynamical cuts, reveals known populations, including the accreted halo, thick disc, thin disc, and in situ halo. The thick disc is characterized by multiple substructures, suggesting it comprises stars formed in diverse environments. Our findings highlight the limited discriminatory power of magnesium in separating accreted and disc stars. Elements such as Ba, Al, Cu, and Sc are critical in distinguishing disc from accreted stars, while Ba, Y, Eu, and Zn differentiate disc and accreted stars from the in situ halo. This study demonstrates the potential power of combining a latent space representation of the data (PCA) with a clustering algorithm (XD) in Galactic archaeology, in providing new insights into the Galaxy’s assembly and evolutionary history.