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Robust, data-driven inference in non-linear cosmostatistics

2012/11/12 by B. D. Wandelt, Benjamin D. Wandelt, Wandelt, Benjamin D. +4
Computer Science · Mathematics · Physics and Astronomy · #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Gaussian Processes and Bayesian Inference #Statistics Education and Methodologies #astro-ph.CO

paper · pdf · doi:10.48550/arxiv.1211.2757

14 pages, 10 figures. Talk given at "Statistical Challenges in Modern Astronomy V," held at Penn State

arxiv created 2012/11/12 · openalex publication_date 2012/11/12 · arxiv updated 2012/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We discuss two projects in non-linear cosmostatistics applicable to very large surveys of galaxies. The first is a Bayesian reconstruction of galaxy redshifts and their number density distribution from approximate, photometric redshift data. The second focuses on cosmic voids and uses them to construct cosmic spheres that allow reconstructing the expansion history of the Universe using the Alcock-Paczynski test. In both cases we find that non-linearities enable the methods or enhance the results: non-linear gravitational evolution creates voids and our photo-z reconstruction works best in the highest density (and hence most non-linear) portions of our simulations.

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