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dc.contributor.advisorFeldman, Hume A
dc.contributor.authorAgarwal, Shankar
dc.date.accessioned2013-02-17T17:39:03Z
dc.date.available2013-02-17T17:39:03Z
dc.date.issued2012-12-31
dc.date.submitted2012
dc.identifier.otherhttp://dissertations.umi.com/ku:12505
dc.identifier.urihttp://hdl.handle.net/1808/10828
dc.description.abstractWe 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.extent130 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsThis item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
dc.subjectPhysics
dc.titlePkANN: Non-Linear Matter Power Spectrum Interpolation through Artificial Neural Networks
dc.typeDissertation
dc.contributor.cmtememberFeldman, Hume A
dc.contributor.cmtememberMedvedev, Mikhail V
dc.contributor.cmtememberMarfatia, Danny
dc.contributor.cmtememberShi, Jack
dc.contributor.cmtememberLerner, David
dc.thesis.degreeDisciplinePhysics & Astronomy
dc.thesis.degreeLevelPh.D.
kusw.oastatusna
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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