Abstract

Tidal streams—remnants of disrupted stellar systems—are powerful tracers of galactic gravitational potentials. While streams in the Milky Way have yielded insights into its dark matter halo thanks to full 6D stellar data, applying this method to external galaxies is more difficult due to the lack of kinematics and projection effects. Individually, photometric-only streams offer limited constraints, but their collective signal can be statistically powerful.

In this talk, we present a novel hierarchical Bayesian framework that uses purely photometric data to constrain the population-level properties of dark matter halos. To achieve this, we constructed STRRINGS , a catalog of long and curved streams around nearby galaxies. Our results show that even without kinematic information, an ensemble of just 50 well-characterized streams can reliably distinguish between oblate, spherical, and prolate halos. This highlights that even purely photometric datasets, when analyzed in aggregate, can yield robust insights into dark matter distributions.

This breakthrough arrives at a critical moment, as upcoming surveys from Euclid and LSST are set to deliver an unprecedented volume of high-quality stream observations. Our approach represents a paradigm shift in how we constrain dark matter properties, ultimately refining our understanding of the universe’s fundamental structure.