  Stellar streams are potentially a very sensitive observational probe of
galactic astrophysics, as well as the dark matter population in the Milky Way.
On the other hand, performing a detailed, high-fidelity statistical analysis of
these objects is challenging for a number of key reasons. Firstly, the
modelling of streams across their (potentially billions of years old) dynamical
age is complex and computationally costly. Secondly, their detection and
classification in large surveys such as Gaia renders a robust statistical
description regarding e.g., the stellar membership probabilities, challenging.
As a result, the majority of current analyses must resort to simplified models
that use only subsets or summaries of the high quality data. In this work, we
develop a new analysis framework that takes advantage of advances in
simulation-based inference techniques to perform complete analysis on complex
stream models. To facilitate this, we develop a new, modular dynamical
modelling code sstrax for stellar streams that is highly accelerated using jax.
We test our analysis pipeline on a mock observation that resembles the GD1
stream, and demonstrate that we can perform robust inference on all relevant
parts of the stream model simultaneously. Finally, we present some outlook as
to how this approach can be developed further to perform more complete and
accurate statistical analyses of current and future data.
