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

Abstract

ABSTRACT 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.