The first generation of stars has little to no metal. The fascinating and rich chemistry that we see from stars results from supernovae and stellar winds since the Big Bang. These stellar yield processes generate and return metals to the interstellar medium from which subsequent generations of stars formed. Since the chemistry of stars that we can see today is recycled from previous generations, the chemical composition of stars essentially serves as the DNA fingerprints of stars. Therefore, by studying the detailed chemistry of stars formed at different times, we can, in principle, reconstruct the “family tree” of stars, tracing the origins of individual stars back to their most ancient stellar “ancestors.”
In recent years, a myriad of method phylogenetic methods has been developed to study the evolutionary histories of species using DNA sequence data. These methods are based on rigorous statistical models of sequence evolution that allow us to infer the tree of life a billion years ago. This advancement has enabled new possibilities to combine these two booming fields (Galactic Archaeology and Bioinformatics) to unravel the history of our own Galaxy, the Milky Way.
Apply phylogenetic techniques to data from millions of stars with their chemical compositions measured and reconstruct the “evolutionary” tree in the Milky Way.
The student will work with A/Prof Yuan-Sen Ting (School of Computing / Astronomy) and Dr Minh Bui (School of Computing / Bioinformatics).
Python programming is essential. Experience in machine learning and data science will be helpful.
machine learning, genealogy, astronomy