Potential projects (necessarily incomplete):

• Globular Clusters for Galactic Archaeology (observations, surveys, big data, dynamical modeling): using the ARCES echelle spectrograph on ARC 3.5m telescope, this student will lead a survey of the Northern Hemisphere globular clusters focusing specifically on the recovery of the heavy, r-process element europium. Based on results from Monty et al. 2024 which showed that Eu is a strong candidate "chemical tag" linking star clusters back to the dwarf galaxies they formed in, this student will combine their data with upcoming data from the 4MOST survey to build a complete picture of the accretion history of the Milky Way.
• Mining the Milky Way Halo using Machine Learning (big data, surveys, machine learning, observations): many different machine learning techniques have been successfully applied to large suverys of the Milky Way (e.g. convolutional neural nets, XGBoost, UMAP), primarily working to transfer information learned from small, high resolution surveys, to larger, lower resolution surveys. This project will focus specifically on investigating ML techniques to search for "needle in a haystack" stars, which possess chemical signatures from unique astrophysical events. These stars will then be followed up with the 3.5m telescope or through American time on 8m telescopes.
• Next Generation Science with Adaptive Optics Instrumentation (simulations, instrumentation): all next generation 30m-class telescopes will be equipped with adaptive optics (AO) sytems. AO seeks to remove the "twinkle" from the star, mitigating the destructive effects of the Earth's atmosphere to allow a telescope to operate as if it was in space (at its diffraction limit). Flowing down system design characteristics to scientific observables is critical both to verify that these instruments will deliver what is promised and to design next-generation surveys to answer critical scientific questions. This project could be tailored to lean more heavily into the science or the instrumentation, with the possibility of designing and building an AO system on an optical bench (likely in collaboration with the School of Electrical and Computer Engineering) or working on the AO system at the Dunn Solar Telescope (in collaboration with Profs Shetye and Jackiewicz)
• Resolving Active Galactic Nuclei (AGN) Imposters (observations): a small, but growing subset of AGN may in fact be AGN-imposters. Some of these AGN-imposters have been well-reproduced through models of young massive clusters, possibly the precursors or globular clusters. Working in collaboration with Prof. Prescott, the student will identify these objects, collect new observations with the 3.5m if necessary and characterise them using modeling techniques to resolve their true nature.
• Predicting Signatures of the First Stars in Dwarf Galaxies (simulations, observations): while we have yet to find a star from the very first generation of stars (Population III) stars (we may never), observations both Locally and in incredibly distant, unresolved galaxies detected with JWST provide clues as to what these stars may have been like. JWST is now providing direct measurements of the composition of gas which was possibly expelled from Population III stars upon their death in ancient galaxies. In collaboration with Prof. Finlator, this student will perform dedicated radiation hydrodynamic simulations of galaxies grounded in observations of the Local Group to explore the validity of First Star signatures at high redshift and make predictions for observations both Locally and at high redshift.
• The Chemodynamics of Exo-planet Host Stars (observations, big data, dynamical modeling): when and how exo-planets began forming in the Universe is highly sensitive to the properties of their host stars (especially the chemical composition). Coupling measurements of 20+ chemical elements with dynamical information, this student will explore exo-planet host star demographics in a new way, comparing and constrasting these systems with our own Solar System to understand if and how it may be unique. In collaboration with Prof. Nielsen, this student will explore existing exo-planet host star data, potentially collecting missing data using the 3.5m ARCES instrument or SONG.