📝 SummarXiv

Abstract

Stellar streams provide one of the most promising avenues for constraining the global mass distribution of the Milky Way and the nature of dark matter (DM). The stream stars' kinematic "track" enables inference of large-scale properties of the DM distribution, while density variations and anomalies provide information about local DM clumps (e.g., from DM subhalos). Using precise astrometric data from the Gaia Mission, which enables clean selections of Milky Way stream stars, we now know of a few streams with perturbations and density anomalies. A full accounting of the density tracks and substructures within all $>100$ Milky Way stellar streams will therefore enable powerful new constraints on DM. However, methods for discovering and characterizing membership of streams are heterogeneous and often highly customized to individual streams. Here, we present a new, flexible framework for modeling stellar stream density and membership. With it, one can empirically model a given stream in a variety of coordinate spaces (\eg on-sky position and velocity) using probability distributions, thereby generating membership probabilities. The most significant improvement over previous methods is the inclusion of off-track or non-Gaussian components to the stream density, meaning we can capture anomalous features (such as the GD-1 steam's spur). We test our model on GD-1, where we characterize previously-known features and provide the largest catalog of probable member stars to date (1689 stars). We then use the derived model to provide measurements of GD-1's density and kinematic tracks, velocity dispersion, as well as its initial and current mass. Our framework (built on JAX and numpyro) provides a path toward uniform analysis of all Milky Way streams, enabling tight constraints on the Galactic mass distribution and its dark matter.