Projects
Selected projects in Galactic archaeology, stellar abundances, and deep learning applications for astronomical surveys.
Identifies a rotating stellar component in the bulge and halo with neural-network classification applied to Gaia DR3.
Uses test-particle simulations, neural-network classification, and physical interpretation to study angular-momentum transfer mechanisms.
Develops a deep-learning framework to identify ex-situ stars using 6D kinematics and actions from Gaia DR3.
Includes Gaia data preprocessing, classifier development and training, and interpretation of ex-situ fractions and origins.
Applies neural networks to estimate stellar parameters and elemental abundances for 1.2 million giants in LAMOST DR8.
The resulting value-added catalog supports studies of the Milky Way's chemical evolution.
In preparation
Project page forthcoming. A detailed public description will be added when ready.