Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-25T16:08:17.999Z Has data issue: false hasContentIssue false
  • English
  • Français

Non-Gaussian inference from non-linear and non-Poisson biased distributed data

Published online by Cambridge University Press:  01 July 2015

Metin Ata ,
Francisco-Shu Kitaura  and
Volker Müller
Metin Ata
Affiliation:
Leibniz Institute for Astrophysics (AIP), An der Sternwarte 16, 14482 Potsdam email: [email protected]
Francisco-Shu Kitaura
Affiliation:
Leibniz Institute for Astrophysics (AIP), An der Sternwarte 16, 14482 Potsdam email: [email protected]
Volker Müller
Affiliation:
Leibniz Institute for Astrophysics (AIP), An der Sternwarte 16, 14482 Potsdam email: [email protected]
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.

We study the statistical inference of the cosmological dark matter density field from non-Gaussian, non-linear and non-Poisson biased distributed tracers. We have implemented a Bayesian posterior sampling computer-code solving this problem and tested it with mock data based on N-body simulations.

Keywords

cosmology: large-scale-structuresmethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 258 - 261
Copyright
Copyright © International Astronomical Union 2015 

References

Carron, J. & Szapudi, I., 2014, MNRAS (Letters), 439, L11Google Scholar
Cen, R. & Ostriker, J. P., 1993, ApJ, 417, 415Google Scholar
Coles, P. & Jones, B. J. T., 1991, MNRAS, 248, 1CrossRefGoogle Scholar
Colombi, S., 1994, ApJ, 435, L536Google Scholar
de l ATorre, S. & Peacock, J. A., 2013, MNRAS, 435, 743Google Scholar
Heß, S., Kitaura, F.-S. & Gottlöber, S., 2013, MNRAS, 435, 2065Google Scholar
Jasche, J. & Kitaura, F.-S., 2010a, MNRAS, 407, 29Google Scholar
Jasche, J. & Wandelt, B. D., 2013, ApJ, 779, 15Google Scholar
Kitaura, F.-S., 2012, Springer Series in Astrostatistics, 143Google Scholar
Kitaura, F.-S., 2012, MNRAS, 420, 2737CrossRefGoogle Scholar
Kitaura, F.-S., 2013, MNRAS (Letters), 429, 1, L84Google Scholar
Kitaura, F.-S. & Enßlin, T. A., 2008, MNRAS, 389, 497Google Scholar
Kitaura, F.-S., Erdogdu, P., Nuza, S. E., Khalatyan, A., Angulo, R. E., Hoffman, Y. & Gottlöber, S., 2012, MNRAS (Letters), 427, L35Google Scholar
Kitaura, F.-S., Gallerani, S., & Ferrara, A., 2012, MNRAS, 420, 61Google Scholar
Kitaura, F.-S., Jasche, J. & Metcalf, R. B., 2010, MNRAS, 403, 589CrossRefGoogle Scholar
Kitaura, F.-S., Yepes, G. & Prada, F., 2014, MNRAS (Letters), 439, L21Google Scholar
Klypin, A. A., Trujillo-Gomez, S., & Primack, J., 2011, ApJ, 740, 102Google Scholar
Neyrinck, M. C., Aragón-Calvo, M. A., Jeong, D., & Wang, X. 2014, MNRAS, 441, 646Google Scholar
Neyrinck, M. C. and Szapudi, I., & Szalay, A. S., 2009, ApJ (Letters), 698L, 90NGoogle Scholar
Saslaw, W. C. & Hamilton, A. J. S., 1984, ApJ, 276, 13Google Scholar
Sheth, R. K., 1995, MNRAS, 274, 21CrossRefGoogle Scholar
Wang, H., Mo, H. J., Yang, X. & van den Bosch, F. C., 2012, MNRAS, 420, 1809Google Scholar