The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution

By Miguel Angel Aragon-Calvo, Bernard J. T. Jones, Rien van de Weygaert, J. V. G. K. A. Institute, J. University. 2007

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Aims. We present here a new method, MMF, for automatically segmenting cosmic structure into its basic components: clusters, fi laments, and walls. Importantly, the segmentation is scale independent, so all structures are identified without prejudice as to the ir size or shape. The method is ideally suited for extracting catalogues of clusters, wa lls, and filaments from samples of galaxies in redshift surve ys or from particles in cosmological N-body simulations: it makes no prior assumptions about the scale or shape of the structures. Methods. Our Multiscale Morphology Filter (MMF) method has been developed on the basis of visualization and feature extraction t echniques in computer vision and medical research. The density or intensity field of the sample is smoothed over a range of scales. Th e smoothed signals are processed through a morphology response filter whose for m is dictated by the particular morphological feature it see ks to extract, and depends on the local shape and spatial coherence of the intensity field. The morphology signal at each location is then defi ned to be the one with the maximum response across the full range of smoothing scales. The success of our method in identifying anisotropic features such as filaments and walls depends critically on the use of an optimally defined intensity field. This is accomplished by applying the DTFE re construction methodology to the sample particle or galaxy distribution. Results. We have tested our MMF Filter against a set of heuristic models of weblike patterns such as are seen in the Megaparsec cosmic matter distribution. To test its effectiveness in the context of more realistic configurations w e also present preliminary results from the MMF analysis of an N-body model. Comparison with alternative prescriptions for feature extraction shows that MMF is a remarkably strong structure finder

Subfields: Astronomy and Astrophysics, Instrumentation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a7b3a24d54273bf002dd38a5d15c0e6171346cdb

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Citations in the corpus

The shape of dark matter haloes in the Aquarius simulations: evolution and memory (2011, background): “…reported so far in the literature (Lemson & Kauffmann 1999; Avila-Reese et al. 2005; Faltenbacher et al. 2005; Altay et al. 2006; Basilakos et al. 2006; Gottlöber & Turchaninov 2006; Patiri et al. 2006; Aragón-Calvo et al. 2007; Hahn et al. 2007; Macciò et al. 2007; Ragone-Figueroa et al. 2010).”
Multiscale phenomenology of the cosmic web (2010, methodology): “The raw output of the MMF is an Object Map O, which defines which pixels belong to a given morphology (see Aragón-Calvo et al. 2007b).”
zobov: a parameter-free void-finding algorithm (2007, methodology): “There are other methods that use tessellation techniques to find clusters (Ramella et al.2001; Barkhouse et al. 2006; Söchting et al. 2006; van Breukelen et al. 2006; Melnyk, Elyiv & Vavilova 2006) or voids (Gaite 2005; Aragón-Calvo et al. 2007).”
THE SPIN AND ORIENTATION OF DARK MATTER HALOS WITHIN COSMIC FILAMENTS (2009, methodology): “These trends agree well with results obtained by Hahn et al. (2007b) and Aragón-Calvo et al. (2007b).”
Evidence for spin alignment of spiral and elliptical/S0 galaxies in filaments (2012, background): “More specifically, Aragón-Calvo et al. (2007b), Trowland, Lewis & Bland-Hawthorn (2012) and Codis et al. (2012) showed that the orientation of the halo spin vector is mass dependent.”
NEXUS: Tracing the cosmic web connection (2012, methodology): “An earlier and simpler version of the method was introduced in cosmology under the name Multiscale Morphology Filter (MMF) in Aragón-Calvo et al. (2007b).”
A Dynamical Classification of the Cosmic Web (2008, methodology): “This is the case of the web classification based on the multi-scale analysis of the Hessian matrix of the density field (Aragón-Calvo et al. 2007) or the skeleton analysis of the density field (Novikov et al. 2006; Sousbie et al. 2008).”
Detecting filamentary pattern in the cosmic web: a catalogue of filaments for the SDSS (2013, background): “…watershed segmentation (Platen, van de Weygaert & Jones 2007; AragónCalvo et al. 2010b), the tessellations (Doroshkevich, Gottlöber & Madsen 1997; Aragón-Calvo et al. 2007b; Aragón-Calvo, van de Weygaert & Jones 2010a; González & Padilla 2010; Sousbie 2011; Sousbie, Pichon & Kawahara…”
Tracing the dark matter sheet in phase space (2011, background): “Configuration space density estimators also play a particularly important role when studying the topology and character of the cosmic web (e.g Schaap & van de Weygaert 2000; Pelupessy et al. 2003; Aragón-Calvo et al. 2007; Colberg et al. 2008; Neyrinck 2008).”
Evolution of the cosmic web (2014, methodology): “Such studies have shown that indeed there is a systematic dependence of halo properties, like shape, spin and formation redshift, on the environment in which they are embedded (Aragón-Calvo et al. 2007b; Hahn et al. 2007a,b, 2009).”
A kinematic classification of the cosmic web (2012, methodology): “This has been often applied independently of any dynamical context (Lemson & Kauffmann 1999; Novikov et al. 2006; Aragón-Calvo et al. 2007; Sousbie et al. 2008).”
Tracing the cosmic web (2017, methodology): “…large class of approaches exploits the morphological and (local) geometric information included in the Hessian of the density, tidal or velocity shear fields (e.g. Aragón-Calvo et al. 2007a; Hahn et al. 2007a; Forero-Romero et al. 2009; Bond et al. 2010a; Libeskind et al. 2012; Cautun et al. 2013).”