Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-15T16:41:20.826Z Has data issue: false hasContentIssue false
  • English
  • Français

Quantifying the Cosmic Web using the Shapefinder diagonistic

Published online by Cambridge University Press:  12 October 2016

Prakash Sarkar
Prakash Sarkar*
Affiliation:
Department of Theoretical Physics, TIFR, Mumbai-400005, India email: prakash@theory.tifr.res.in
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

One of the most successful method in quantifying the structures in the Cosmic Web is the Minkowski Functionals. In 3D, there are four minkowski Functionals: Area, Volume, Integrated Mean Curvature and the Integrated Gaussian Curvature. For defining the Minkowski Functionals one should define a surface. We have developed a method based on Marching cube 33 algorithm to generate a surface from a discrete data sets. Next we calculate the Minkowski Functionals and Shapefinder from the triangulated polyhedral surface. Applying this methodology to different data sets , we obtain interesting results related to geometry, morphology and topology of the large scale structure

Keywords

methods: data analysis - galaxies: statistics - large-scale structure of universe
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 11 , Symposium S308: The Zeldovich Universe: Genesis and Growth of the Cosmic Web , June 2014 , pp. 250 - 253
Copyright
Copyright © International Astronomical Union 2016 

References

Bharadwaj, S., Sahni, V., Sathyaprakash, B. S., Shandarin, S. F., & Yess, C. 2000, ApJ, 528, 21 CrossRefGoogle Scholar
Chernyaev, E. V. 1995, Technical report, Marching Cubes 33: Construction of Topologically Correct IsosurfacesGoogle Scholar
Kim, J., Park, C., Rossi, G., Lee, S. M., Gott, J. R., & , III, 2011, Journal of Korean Astronomical Society, 44, 217 Google Scholar
Mecke, K. R., Buchert, T., & Wagner, H. 1994, A&A, 288, 697 Google Scholar
Park, C., Choi, Y.-Y., Kim, J., et al. 2012, ApJ(letters), 759, L7 Google Scholar
Rusinkiewicz, S. 2004, Symposium on 3D Data Processing, Visualization, and Transmission Google Scholar
Sahni, V., Sathyaprakash, B. S., & Shandarin, S. F. 1998, ApJ(letters), 495, L5 Google Scholar
Sheth, J. V., 2006, Ph.D. Thesis Google Scholar