The dynamical memory of tidal stellar streams: joint inference of the Galactic potential and the progenitor of GD-1 with flow matching
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 matc...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
March 2026
|
| In: |
Astronomy and astrophysics
Year: 2026, Volume: 707, Pages: 1-12 |
| ISSN: | 1432-0746 |
| DOI: | 10.1051/0004-6361/202558358 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1051/0004-6361/202558358 Verlag, kostenfrei, Volltext: https://www.aanda.org/articles/aa/abs/2026/03/aa58358-25/aa58358-25.html 
|
| Author Notes: | Giuseppe Viterbo and Tobias Buck |
| Summary: | 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. |
|---|---|
| Item Description: | Online veröffentlicht: 24. März 2026 Gesehen am 23.04.2026 |
| Physical Description: | Online Resource |
| ISSN: | 1432-0746 |
| DOI: | 10.1051/0004-6361/202558358 |