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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: [email protected]
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Abstract

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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