paper

Constraining the population of dark matter halo shapes using hierarchical inference with extragalactic stellar streams

arXiv:2601.15373 · doi:10.1093/mnras/stag1161

Abstract

Stellar streams, the debris of tidally disrupted satellites, trace their host's gravitational potential and thus probe dark matter halo structure. While six-dimensional phase-space data of Galactic streams enable precise dark matter halo modelling in the Milky Way, streams around external galaxies are typically available only as low surface brightness features without kinematics (i.e. two-dimensional photometric data), providing only weak constraints when considered individually. We present a hierarchical Bayesian framework that infers the population distribution of halo flattening using only projected stream tracks. Streams are forward-modelled in StreaMAX, a new JAX-accelerated particle-spray package that achieves orders of magnitude faster stream generation when compared to traditional methods. For each stream we fit an axisymmetric dark matter halo model and obtain a posterior on the flattening. These posteriors are then combined through hierarchical reweighting to constrain the population distribution. Using mock data, we show that individual fits recover the correct flattening with modest precision and exhibit projection-induced multi-modalities. Nevertheless, aggregating these fits yields accurate and confident constraints on the underlying population distribution of dark matter halo morphologies, clearly distinguishing between oblate, spherical, and prolate populations. The total computational cost scales linearly with sample size. Our results demonstrate that ensembles of purely photometric streams carry sufficient information to constrain dark matter halo shapes in external galaxies at the population level. With the forthcoming samples from Euclid and Rubin/LSST, this approach offers a practical path to population-level inferences of halo morphology without any kinematic measurements.

arXiv commentAccepted for publication in MNRAS on 2026 June 12. 18 pages, 12 figures

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