The dynamical memory of tidal stellar streams

Joint inference of the Galactic potential and the progenitor of GD-1 with flow matching

¹ Universität Heidelberg, Interdisziplinäres Zentrum für Wissenschaftliches Rechnen (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
² Universität Heidelberg, Zentrum für Astronomie, Institut für Theoretische Astrophysik, Albert-Ueberle-Straße 2, 69120 Heidelberg, Germany

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 2 December 2025
Accepted: 18 February 2026

Abstract

Context. Stellar streams offer one of the most sensitive probes of the Milky Way’s gravitational potential, as their phase-space morphology encodes both the tidal field of the host galaxy and the internal structure of their progenitors. In this work, we introduce a framework that leverages flow matching and simulation-based inference (SBI) to jointly infer the parameters of the GD-1 progenitor and the global properties of the Milky Way potential.

Aims. Our aim is to move beyond traditional techniques (e.g., orbit-fitting and action-angle methods) by constructing a fully Bayesian likelihood-free posterior over host galaxy parameters and progenitor properties, thereby capturing the intrinsic coupling between tidal stripping dynamics and the underlying potential.

Methods. To achieve this, we generated a large suite of mock GD-1-like streams using our differentiable N-body code ODISSEO, sampling self-consistent initial conditions from a Plummer sphere and evolving them in a flexible Milky Way potential model. We then applied conditional flow matching to learn the vector field that transports a base Gaussian distribution into the posterior p(θ | d), enabling efficient amortized inference directly from stream phase-space data.

Results. We demonstrate that our method successfully recovers the true parameters of a fiducial GD-1 simulation, producing well-calibrated posteriors and accurately reproducing parameter degeneracies arising from progenitor-host interactions. Our results highlight the power of modern generative models for dynamical inference and provide a scalable pathway toward jointly constraining Galactic structure and the origins of stellar streams.

Conclusions. Flow matching provides a powerful, flexible framework for Galactic archaeology. Our approach enables joint inference on progenitor and Galactic parameters, capturing complex dependencies that are difficult to model with classical likelihood-based methods. This work paves the way for fully simulation-driven dynamical inference using Gaia and upcoming surveys.