A machine–vision method for automatic classification of stellar halo substructure. (arXiv:1811.10613v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Hendel_D/0/1/0/all/0/1">David Hendel</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Johnston_K/0/1/0/all/0/1">Kathryn V. Johnston</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Patra_R/0/1/0/all/0/1">Rohit K. Patra</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sen_B/0/1/0/all/0/1">Bodhisattva Sen</a>
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 halos.
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 halos.
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