Sarah Martell's projects - Galactic archaeology with large data sets
Galactic archaeology: Galactic archaeology is the study of the Milky Way's structure and evolution, based on detailed information about the orbits and chemical compositions of the stars in it. One major goal of Galactic archaeology is "chemical tagging", which uses the elemental abundance patterns of stars to identify groups of stars that formed together and then dispersed across the Galaxy. With the original groups of stars identified, we can the look directly at the rate of star formation, the buildup of heavy elements and the migration of stars through the Milky Way over time.
Large data sets: There are multiple large survey projects collecting data right now, including two led at UNSW: GALAH, collecting high-resolution spectra for a million stars, and FunnelWeb, collecting low-resolution spectra for essentially all of the 6.5 million bright stars in the Southern sky. Each survey project has its own focus on particular populations of stars and types of data collected, making them very complementary, such that the combination of them is very powerful. Current large data sets have collected data for tens to hundreds of thousands of stars, and future surveys will target millions of stars.
Research projects: Within the Galactic research group, undergraduate, Honours and PhD projects all focus on some aspect of the Milky Way, typically using large survey data sets. Previous and ongoing projects include:
- Searching for stars with unusually high lithium abundances, to understand their origins
- Using orbital kinematics and chemical tagging to identify stars that formed in star clusters and later escaped
- Developing statistical methods to identify stars with unusual spectra in large survey data sets
- Combining multiple survey data sets to create large-scale maps of the Milky Way and investigate its evolutionary history
If you want to join the Galactic research group: There are many open questions in Galactic archaeology, which can be addressed with observational, computational or statistical methods. Your background knowledge is less important than your interest in building new skills and new code. Feel free to talk to Dr. Martell (email@example.com) about possible research projects.