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

TL;DR

This paper introduces Albatross, a scalable simulation-based inference pipeline utilizing a new modular dynamical modeling code, sstrax, to analyze complex stellar streams in the Milky Way with high fidelity and efficiency.

Contribution

The work presents a novel, accelerated inference framework and a new dynamical modeling code for detailed analysis of stellar streams, overcoming previous computational and statistical challenges.

Findings

Successfully performed robust inference on a mock GD1-like stream.

Demonstrated the pipeline's ability to analyze complex stream models simultaneously.

Outlined future developments for more comprehensive statistical analyses.

Abstract

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.