2025ApJ...995...15C

The Gaia mission has led to the discovery of over 100 stellar streams in the Milky Way, most of which likely originated from globular clusters (GCs). As the upcoming wide-field surveys can potentially continue to increase the number of known streams, there is a growing need to shift focus from manual detection of individual streams to automated detection methods that prioritize both quality and quantity. Traditional techniques rely heavily on the visual expectation that GC streams are dynamically cold and thin. This assumption does not hold for all streams, whose morphologies and kinematics can vary significantly with the progenitor's mass and orbit. As a result, these methods are biased toward a subset of the whole stream population, with often unquantified purity and completeness. In this work, we present StarStream, an automatic stream detection algorithm based on a physics-inspired model rather than visual expectation. Our method provides a more accurate prediction of stream stars in the multidimensional space of observables, while using fewer free parameters to account for the diversity of streams. Applied to a mock GC stream catalog tailored for the Gaia DR3 data set, our algorithm achieves both purity and completeness of at least 65% at Galactic latitudes ∣b∣ > 30^∘.