The goal of my work is to study the evolution of dense regions in the Milky Way and link the Galactic and extragalactic observations to understand galaxy evolution. The presentation will be divided into two main projects.
The first project introduces a novel approach employing the neural network model DD-Payne to derive stellar abundances from MUSE observations. It overcomes the limitations imposed by fiber collisions in fiber-fed spectrographs and enables a comprehensive analysis of the formation history of dense regions, including the Galactic bulge and globular cluster cores. I will also present preliminary results from MUSE observations towards Baade's window, where we identified over 100 Main-Sequence Turn-Off (MSTO) stars and determined their ages.
The second project introduces GalCraft, a flexible code that generates mock 3D datacubes of the Milky Way as observed by an integral-field spectrograph (i.e., MUSE). We create these using a synthetic Milky Way catalog from E-Galaxia, in combination with Single Stellar Population (SSP) models like MILES and PEGASE-HR. The goal is to compare these mock Milky Way datacubes with existing integral-field spectroscopy (IFS) observations on external galaxies and provide constraints on the formation and evolution history of disk galaxies.
I am a PhD student at the University of Sydney supervised by Joss Bland-Hawthorn, Michael Hayden and Sanjib Sharma and I just submitted my thesis on June-30. I obtained my bachelor’s degree in Nanjing University in 2019, supervised by Jiwei Xie. My research interests include chemical evolution of the Milky Way; dense regions including the Milky Way bulge, globular clusters; stellar abundances measurements using machine learning; linking the Galactic and extra-galactic studies to understand disk galaxy evolution.