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Tidal debris structures formed from disrupted satellites contain importantclues about the assembly histories of galaxies. To date, studies of thesestructures have been hampered by reliance on by-eye identification andmorphological classification which leaves their interpretation significantlyuncertain. In this work we present a new machine-vision technique based on theSubspace-Constrained Mean Shift (SCMS) algorithm which can perform these tasksautomatically. SCMS finds the location of the high-density `ridges' that definesubstructure morphology. After identification, the coefficients of anorthogonal series density estimator are used to classify points on the ridgesas part of a continuum between shell-like or stream-like debris, from which aglobal morphological classification can be determined. We dub this procedureSubspace Constrained Unsupervised Detection of Structure (SCUDS). By applyingthis tool to controlled N--body simulations of minor mergers we demonstratethat the extracted classifications correspond to the well--understoodunderlying physics of phase mixing. The application of SCUDS to resolvedstellar population data from near-future surveys will inform our understandingof the buildup of galaxies stellar halos.

A machine--vision method for automatic classification of stellar halo substructure