Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70342

Decoding the Universe: Tools for Optimal Extraction of Information from Galaxy Surveys

File Size Format  
Repp hawii 0085A 10769.pdf 8.74 MB Adobe PDF View/Open

Item Summary

Title:Decoding the Universe: Tools for Optimal Extraction of Information from Galaxy Surveys
Authors:Repp, Andrew
Contributors:Szapudi, István (advisor)
Astronomy (department)
Keywords:Astronomy
Cosmology
Information
Statistics
Date Issued:2020
Publisher:University of Hawai'i at Manoa
Abstract:Future galaxy surveys (whether space-based, like Euclid and the Roman Space Telescope, or ground-based like the Rubin Observatory’s LSST) will provide an unprecedented amount of data regarding the distribution of galaxies. This distribution encodes significant cosmological information, including neutrino mass and the nature of dark energy. However, the very power of these surveys can become problematic, in that they will probe scales ($\la 10h^{-1}$Mpc) on which the standard analysis techniques (using the galaxy power spectrum) are blind to significant portions of the cosmological information inherent in the data. This work develops two categories of tools for decoding this hidden information.
First, it applies the theory of sufficient statistics to galaxy surveys, providing prescriptions for these statistics, which one can then fit to observations. In particular, it provides, for any near-concordance cosmology, prescriptions for (a) the sufficient-statistics dark matter power spectrum $P_A(k)$, (b) the matter probability distribution function $\mathcal{P}(\delta)$, and (c) the sufficient statistics galaxy power spectrum $P_{A^*}(k)$.
Second, it pioneers techniques for fitting cosmological models to counts-in-cells (CIC) galaxy probability distributions; in particular, it (a) proposes and validates a galaxy bias model applicable to scales as small as $2h^{-1}$Mpc, (b) determines values for otherwise-degenerate parameters ($\sigma_8$ and galaxy bias $b$) by fitting to observed CIC in SDSS data, and (c) derives and verifies formulae for the CIC covariance matrix.
Together, these tools allow maximally efficient extraction of cosmological information from the data returned by future galaxy surveys.
Pages/Duration:267 pages
URI:http://hdl.handle.net/10125/70342
Rights:All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections: Ph.D. - Astronomy


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.