Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-25T04:21:30.855Z Has data issue: false hasContentIssue false
  • English
  • Français

Bayesian large-scale structure inference: initial conditions and the cosmic web

Published online by Cambridge University Press:  01 July 2015

Florent Leclercq  and
Benjamin Wandelt
Florent Leclercq
Affiliation:
Institut d'Astrophysique de Paris (IAP), UMR 7095, CNRS - UPMC Université Paris 6, 98bis boulevard Arago, F-75014 Paris, France Institut Lagrange de Paris (ILP), Sorbonne Universités, 98bis boulevard Arago, F-75014 Paris, France École polytechnique ParisTech, Route de Saclay, F-91128 Palaiseau, France
Benjamin Wandelt
Affiliation:
Institut d'Astrophysique de Paris (IAP), UMR 7095, CNRS - UPMC Université Paris 6, 98bis boulevard Arago, F-75014 Paris, France Institut Lagrange de Paris (ILP), Sorbonne Universités, 98bis boulevard Arago, F-75014 Paris, France Departments of Physics and Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL 61801 emails: [email protected], [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 describe an innovative statistical approach for the ab initio simultaneous analysis of the formation history and morphology of the large-scale structure of the inhomogeneous Universe. Our algorithm explores the joint posterior distribution of the many millions of parameters involved via efficient Hamiltonian Markov Chain Monte Carlo sampling. We describe its application to the Sloan Digital Sky Survey data release 7 and an additional non-linear filtering step. We illustrate the use of our findings for cosmic web analysis: identification of structures via tidal shear analysis and inference of dark matter voids.

Keywords

large-scale structure of universemethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 1 - 4
Copyright
Copyright © International Astronomical Union 2015 

References

Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2008, Astrophys. J. Supp., 175, 297Google Scholar
Blanton, M. R., Schlegel, D. J., Strauss, M. A., et al. 2005, AJ, 129, 2562Google Scholar
Bellman, R. E. 1961, Adaptive Control Processes: A Guided Tour (Princeton University Press)CrossRefGoogle Scholar
Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. 1987, Physics Letters B, 195, 216Google Scholar
Hahn, O., Porciani, C., Carollo, C. M., & Dekel, A. 2007, Mon. Not. R. Astron. Soc., 375, 489Google Scholar
Jasche, J. & Wandelt, B. D. 2013, Mon. Not. R. Astron. Soc., 432, 894Google Scholar
Jasche, J. & Wandelt, B. D. 2013, ApJ, 779, 15CrossRefGoogle Scholar
Jasche, J., Leclercq, F., & Wandelt, B. D. 2015, JCAP, 1, 036Google Scholar
Kitaura, F.-S. 2013, Mon. Not. R. Astron. Soc., 429, L84Google Scholar
Leclercq, F., Jasche, J., Sutter, P. M., Hamaus, N., & Wandelt, B. 2015, JCAP, 3, 047Google Scholar
Leclercq, F., Jasche, J., & Wandelt, B. 2015, arXiv:1502.02690Google Scholar
Sutter, P. M., Lavaux, G., Hamaus, N., et al. 2014, Mon. Not. R. Astron. Soc., 442, 462Google Scholar
Sutter, P. M., Lavaux, G., Hamaus, N., et al. 2015, Astronomy and Computing, 9, 1Google Scholar
Padmanabhan, N., Schlegel, D. J., Finkbeiner, D. P., et al. 2008, ApJ, 674, 1217Google Scholar
Wang, H., Mo, H. J., Yang, X., & van den Bosch, F. C. 2013, ApJ, 772, 63Google Scholar