2019MNRAS.486.3604H

Tidal debris structures formed from disrupted satellites contain important clues about the assembly histories of galaxies. To date, studies of these structures have been hampered by reliance on by-eye identification and morphological classification which leaves their interpretation significantly uncertain. In this work, we present a new machine-vision technique based on the Subspace-Constrained Mean Shift (SCMS) algorithm which can perform these tasks automatically. SCMS finds the location of the high-density 'ridges' that define substructure morphology. After identification, the coefficients of an orthogonal series density estimator are used to classify points on the ridges as part of a continuum between shell-like or stream-like debris, from which a global morphological classification can be determined. We dub this procedure Subspace-Constrained Unsupervised Detection of Structure (SCUDS). By applying this tool to controlled N-body simulations of minor mergers, we demonstrate that the extracted classifications correspond to the well-understood underlying physics of phase mixing. The application of SCUDS to resolved stellar population data from near-future surveys will inform our understanding of the buildup of galaxies' stellar haloes.