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Detecting multi-scale filaments in galaxy distribution

Published online by Cambridge University Press:  01 July 2015

Elmo Tempel
Elmo Tempel*
Affiliation:
Tartu Observatory, Observatooriumi 1, 61602 Tõravere, Estonia email: [email protected] National Institute of Chemical Physics and Biophysics, Rävala pst 10, 10143 Tallinn, Estonia
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Abstract

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The main feature of the spatial large-scale galaxy distribution is its intricate network of galaxy filaments. This network is spanned by the galaxy locations that can be interpreted as a three-dimensional point distribution. The global properties of the point process can be measured by different statistical methods, which, however, do not describe directly the structure elements. The morphology of the large-scale structure, on the other hand, is an important property of the galaxy distribution. Here, we apply an object point process with interactions (the Bisous model) to trace and extract the filamentary network in the presently largest galaxy redshift survey, the Sloan Digital Sky Survey (SDSS data release 10). We search for multi-scale filaments in the galaxy distribution that have a radius of about 0.5, 1.0, 2.0, and 4.0 h−1 Mpc. We extract the spines of the filamentary network and divide the detected network into single filaments.

Keywords

Methods: data analysismethods: statisticallarge-scale structure of Universe
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 45 - 47
Copyright
Copyright © International Astronomical Union 2015 

References

Ahn, C. P., Alexandroff, R., Allende Prieto, C., et al. 2014, ApJS, 211, 17Google Scholar
Bond, J. R., Kofman, L., & Pogosyan, D. 1996, Nature 380, 603CrossRefGoogle Scholar
Jõeveer, M. & Einasto, J. 1978, in Longair, M. S., Einasto, J. (eds.), Large Scale Structures in the Universe, Proc. IAU Symposium No. 79, 241CrossRefGoogle Scholar
Stoica, R. S., Gregori, P., & Mateu, J. 2005, Stochastic Processes and their Applications, 115, 1860CrossRefGoogle Scholar
Stoica, R. S., Martínez, V. J., & Saar, E. 2007, Journal of the Royal Statistical Society Series C, 56, 459Google Scholar
Stoica, R. S., Martínez, V. J., & Saar, E. 2010, A&A, 510, A38Google Scholar
Tempel, E., Stoica, R. S., Martínez, V. J., Liivamägi, L. J., Castellan, G., & Saar, E. 2014a, MNRAS, 438, 3465CrossRefGoogle Scholar
Tempel, E., Tamm, A., Gramann, M., Tuvikene, T., Liivamägi, L. J., Suhhonenko, I., Kipper, R., Einasto, M., & Saar, E. 2014b, A&A, 566, A1Google Scholar
York, D. G., Adelman, J., Anderson, J. E. Jr., et al. 2000, AJ, 120, 1579Google Scholar