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Stellar streams are potentially a very sensitive observational probe ofgalactic astrophysics, as well as the dark matter population in the Milky Way.On the other hand, performing a detailed, high-fidelity statistical analysis ofthese objects is challenging for a number of key reasons. Firstly, themodelling of streams across their (potentially billions of years old) dynamicalage is complex and computationally costly. Secondly, their detection andclassification in large surveys such as Gaia renders a robust statisticaldescription regarding e.g., the stellar membership probabilities, challenging.As a result, the majority of current analyses must resort to simplified modelsthat use only subsets or summaries of the high quality data. In this work, wedevelop a new analysis framework that takes advantage of advances insimulation-based inference techniques to perform complete analysis on complexstream models. To facilitate this, we develop a new, modular dynamicalmodelling code sstrax for stellar streams that is highly accelerated using jax.We test our analysis pipeline on a mock observation that resembles the GD1stream, and demonstrate that we can perform robust inference on all relevantparts of the stream model simultaneously. Finally, we present some outlook asto how this approach can be developed further to perform more complete andaccurate statistical analyses of current and future data.

Albatross: A scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way