A differentiable forward model for weakly perturbed stellar streams: substructure forecasts from density and kinematics spectra

Abstract: Stellar streams are a promising way to gravitationally detect low-mass substructure, since their low dynamical temperature makes them retain the imprint of weak gravitational perturbations. We develop a fast, differentiable forward model for perturbed stellar streams in the diffusion regime, where the stream is heated by many small velocity kicks rather than by a few strong encounters. The substructure population enters only through its power spectrum, so the computational cost is insensitive to the number of perturbers, and alternative dark matter models and/or baryonic perturbers can be explored by changing this single input. We validate the simulations against analytical predictions, then forecast the sensitivity of a GD-1-like stream to the substructure power spectrum, adding to the stream density the full kinematics, both proper motions and the radial velocity. Kinematic information tightens the constraints by a factor of $\sim 3$-$5$ relative to density alone, improving the precision on the dark matter free-streaming cutoff scale from $\sim 1.2$ dex to $\sim 0.25$ dex at a fiducial value of $M_{\rm hm} = 10^6 M_\odot$ for a $5$ Gyr stream. A single well-measured stream could thus constrain dark matter competitively with current limits from strong lensing and satellite counts.