We present here a new method, MMF, for automatically segmenting cosmic
structure into its basic components: clusters, filaments, and walls.
Importantly, the segmentation is scale independent, so all structures are
identified without prejudice as to their size or shape. The method is ideally
suited for extracting catalogues of clusters, walls, and filaments from samples
of galaxies in redshift surveys or from particles in cosmological N-body
simulations: it makes no prior assumptions about the scale or shape of the
structures.}Comment: Replacement with higher resolution figures. 28 pages, 17 figures. For
  Full Resolution Version see:
  http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pd

Aragón-Calvo

Arbabi-Bidgoli

B. J. T. Jones

Babul

Basilakos

Bernardeau

Bhavsar

Bond

Braun

Chiang

Colberg

Coles

Colombi

de Lapparent

Delaunay

Dolag

Doré

Florack

Gaztañaga

Gott

Graham

Hahn

Hoyle

J. M. van der Hulst

Jones

Kauffmann

Klypin

Lachieze-Rey

Lindeberg

M. A. Aragón-Calvo

Marr

Martínez

Mecke

Mähönen

Novikov

Patiri

Pimbblet

Plionis

Qian

R. van de Weygaert

Ryden

Saar

Sahni

Sato

Schaap

Schmalzing

Schmidt

Shandarin

Sibson

Springel

Stoica

Szapudi

van de Weygaert

Voronoi

White

York

Zel'dovich

English

arXiv

Aragon-Calvo, M. A.

Jones, B. J. T.

van de Weygaert, R.

van der Hulst, J. M.

University of Groningen Research Database

The multiscale morphology filter:identifying and extracting spatial patterns in the galaxy distribution

University of Groningen

The multiscale morphology filter:Identifying and extracting spatial patterns in the galaxy distribution

Aims. We present here a new method, MMF, for automatically segmenting cosmic structure into its basic components: clusters, filaments, and walls. Importantly, the segmentation is scale independent, so all structures are identified without prejudice as to their size or shape. The method is ideally suited for extracting catalogues of clusters, walls, and filaments from samples of galaxies in redshift surveys 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 techniques in computer vision and medical research. The density or intensity field of the sample is smoothed over a range of scales. The smoothed signals are processed through a morphology response filter whose form is dictated by the particular morphological feature it seeks to extract, and depends on the local shape and spatial coherence of the intensity field. The morphology signal at each location is then defined 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 reconstruction 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 we 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.</p

ARTS repository - University of Groningen

Aims.We present here a new method, MMF, for automatically segmenting cosmic structure into its basic 
 components: clusters, filaments, and walls. Importantly, the segmentation is scale independent, 
 so all structures are identified without prejudice as to their size or shape.
 The method is ideally suited for extracting catalogues of clusters, walls, and 
 filaments from samples of galaxies in redshift surveys 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 techniques in computer vision and medical
 research. The density or intensity field of the sample is smoothed over a range of 
 scales. The smoothed signals are processed through a morphology response 
 filter whose form is dictated by the particular morphological feature it seeks to extract, 
 and depends on the local shape and spatial coherence of the intensity field. The morphology 
 signal at each location is then defined 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 reconstruction 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 we 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 finde

EDP Sciences OAI-PMH repository (1.2.0)

The multiscale morphology filter: 
 identifying and extracting spatial patterns in the galaxy distribution

Proceedings - University of Groningen

Crossref

The multiscale morphology filter: identifying and extracting spatial patterns in the galaxy distribution

Aims. We present here a new method, MMF, for automatically segmenting cosmic structure into its basic components: clusters, filaments, and walls. Importantly, the segmentation is scale independent, so all structures are identified without prejudice as to their size or shape. The method is ideally suited for extracting catalogues of clusters, walls, and filaments from samples of galaxies in redshift surveys 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 techniques in computer vision and medical research. The density or intensity field of the sample is smoothed over a range of scales. The smoothed signals are processed through a morphology response filter whose form is dictated by the particular morphological feature it seeks to extract, and depends on the local shape and spatial coherence of the intensity field. The morphology signal at each location is then defined 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 reconstruction 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 we 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

NARCIS 

The multiscale morphology filter: Identifying and extracting spatial patterns in the galaxy distribution

Dissertations of the University of Groningen

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

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

Abstract

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University of Groningen Research Database

University of Groningen

ARTS repository - University of Groningen

EDP Sciences OAI-PMH repository (1.2.0)

Proceedings - University of Groningen

Crossref

University of Groningen

NARCIS

Dissertations of the University of Groningen