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PkANN: Non-Linear Matter Power Spectrum Interpolation through Artificial Neural Networks
dc.contributor.advisor | Feldman, Hume A | |
dc.contributor.author | Agarwal, Shankar | |
dc.date.accessioned | 2013-02-17T17:39:03Z | |
dc.date.available | 2013-02-17T17:39:03Z | |
dc.date.issued | 2012-12-31 | |
dc.date.submitted | 2012 | |
dc.identifier.other | http://dissertations.umi.com/ku:12505 | |
dc.identifier.uri | http://hdl.handle.net/1808/10828 | |
dc.description.abstract | We investigate the interpolation of power spectra of matter fluctuations using artificial neural networks (ANNs). We present a new approach to confront small-scale non-linearities in the matter power spectrum. This ever-present and pernicious uncertainty is often the Achilles&rsquo heel in cosmological studies and must be reduced if we are to see the advent of precision cosmology in the late-time Universe. We detail how an accurate interpolation of the matter power spectrum is achievable with only a sparsely sampled grid of cosmological parameters. We show that an optimally trained ANN, when presented with a set of cosmological parameters (&Omegam h2, &Omegab h2, ns, w0, &sigma8, ∑m&nu and z), can provide a worst-case error &le 1 per cent (for redshift z &le 2) fit to the non-linear matter power spectrum deduced through large-scale N-body simulations, for modes up to k &le 0.9 h Mpc-1. Our power spectrum interpolator, which we label &lsquo PkANN &rsquo, is designed to simulate a range of cosmological models including massive neutrinos and dark energy equation of state w0 ≠ -1. PkANN is accurate in the quasi-non-linear regime (0.1 h Mpc-1 &le k &le 0.9 h Mpc-1) over the entire parameter space and marks a significant improvement over some of the current power spectrum calculators. The response of the power spectrum to variations in the cosmological parameters is explored using PkANN. Using a compilation of existing peculiar velocity surveys, we investigate the cosmic Mach number statistic and show that PkANN not only successfully accounts for the non-linear motions on small scales, but also, unlike N-body simulations which are computationally expensive and/or infeasible, it can be an extremely quick and reliable tool in interpreting cosmological observations and testing theories of structure-formation. | |
dc.format.extent | 130 pages | |
dc.language.iso | en | |
dc.publisher | University of Kansas | |
dc.rights | This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author. | |
dc.subject | Physics | |
dc.title | PkANN: Non-Linear Matter Power Spectrum Interpolation through Artificial Neural Networks | |
dc.type | Dissertation | |
dc.contributor.cmtemember | Feldman, Hume A | |
dc.contributor.cmtemember | Medvedev, Mikhail V | |
dc.contributor.cmtemember | Marfatia, Danny | |
dc.contributor.cmtemember | Shi, Jack | |
dc.contributor.cmtemember | Lerner, David | |
dc.thesis.degreeDiscipline | Physics & Astronomy | |
dc.thesis.degreeLevel | Ph.D. | |
kusw.oastatus | na | |
kusw.oapolicy | This item does not meet KU Open Access policy criteria. | |
kusw.bibid | 8085916 | |
dc.rights.accessrights | openAccess |