Speaker
Description
Recent advances in machine learning (ML), particularly neural density
estimation like normalizing flows, diffusion models, and flow
matching, have opened new doors for high-precision, model-independent
density estimation.
These techniques are highly valuable for galactic dynamics studies, as
they allow us to estimate the distribution of stars in phase space
(position and velocity) without relying on traditional simplified
models.
By combining these ML-based stellar density estimates and equation of
motion solvers for inferring gravitational fields, we can measure the
local dark matter density in a model-independent way.
This talk presents new research opportunities along this direction,
focusing on modeling objects in our local universe using neural
networks and using them for understanding local galactic dark matter
distribution.
We anticipate that these modern machine learning-based approaches will
allow us to fully utilize the potential of current and future
astronomical catalogs, significantly improving our understanding of
dark matter in the local universe.
