  We present a data-driven method for reconstructing the galactic acceleration
field from phase-space measurements of stellar streams. Our approach is based
on a flexible and differentiable fit to the stream in phase-space, enabling a
direct estimate of the acceleration vector along the stream. Reconstruction of
the local acceleration field can be applied independently to each of several
streams, allowing us to sample the acceleration field due to the underlying
galactic potential across a range of scales. Our approach is methodologically
different from previous works, since a model for the gravitational potential
does not need to be adopted beforehand. Instead, our flexible
neural-network-based model treats the stream as a collection of orbits with a
locally similar mixture of energies, rather than assuming that the stream
delineates a single stellar orbit. Accordingly, our approach allows for
distinct regions of the stream to have different mean energies, as is the case
for real stellar streams. Once the acceleration vector is sampled along the
stream, standard analytic models for the galactic potential can then be rapidly
constrained. We find our method recovers the correct parameters for a
ground-truth triaxial logarithmic halo potential when applied to simulated
stellar streams. Alternatively, we demonstrate that a flexible potential can be
constrained with a neural network, though standard multipole expansions can
also be constrained. Our approach is applicable to simple and complicated
gravitational potentials alike, and enables potential reconstruction from a
fully data-driven standpoint using measurements of slowly phase-mixing tidal
debris.
