Speaker
Description
Stellar streams — the tidally disrupted remnants of globular clusters and dwarf galaxies — serve as sensitive dynamical tracers of the Milky Way's gravitational potential. Inferring the potential's parameters from an ensemble of observed streams is naturally cast as a large-scale hierarchical Bayesian problem: each stream constrains local orbital properties while all streams jointly inform global potential parameters such as halo mass, shape, and radial profile. Classical inference approaches like MCMC become computationally intractable at this scale due to the cost of N-body or test-particle simulations, the high dimensionality of the joint parameter space, and the absence of a tractable likelihood. We address these challenges by adopting a simulation-based inference (SBI) framework built on compositional score matching with score-based diffusion models. Specifically, we leverage hierarchical amortized Bayesian inference, training separate global and local diffusion-based score networks on flat, non-hierarchical simulations of individual streams. At inference time, the compositional score is assembled across all observed streams to jointly recover the full posterior over global potential parameters and stream-specific orbital parameters. This approach enables rapid, uncertainty-aware reconstruction of the Milky Way potential from multi-stream data, bypassing the need for exhaustive joint simulations of multiple streams at once.