2007/05/18 by Miguel A. Aragón-Calvo, Miguel A. Aragon-Calvo, Bernard J. T. Jones +2
Physics and Astronomy · #Artificial intelligence #Astronomy and Astrophysical Research #Astrophysics #Computer science #Computer vision #Filter (signal processing) #Galaxies: Formation, Evolution, Phenomena #Galaxy #Geology #Morphology (biology) #Pattern recognition (psychology) #Physics #Redshift #Scale (ratio) #Scientific Research and Discoveries #Segmentation #Statistical physics #astro-ph
paper · pdf · doi:10.1051/0004-6361:20077880
published as Astron.Astrophys.474:315,2004 · Replacement with higher resolution figures. 28 pages, 17 figures. For Full Resolution Version see: http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pdf
arxiv created 2007/05/18 · openalex publication_date 2007/08/13 · arxiv updated 2019/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.