Advanced Statistical Methods for Astrophysical Probes of Cosmology
Lieferbar innerhalb von 2-3 Tagen
BeschreibungThis thesis explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations. Bayesian model selection provides a measure of how good models in a set are relative to each other - but what if the best model is missing and not included in the set? Bayesian Doubt is an approach which addresses this problem and seeks to deliver an absolute rather than a relative measure of how good a model is. Supernovae type Ia were the first astrophysical observations to indicate the late time acceleration of the Universe - this work presents a detailed Bayesian Hierarchical Model to infer the cosmological parameters (in particular dark energy) from observations of these supernovae type Ia.
Dark energy and apparent late time acceleration.
Bayesian Doubt: Should we doubt the Cosmological Constant?.
Bayesian parameter inference for SNeIa data.
Robustness to Systematic Error for Future Dark Energy Probes.
Summary and Conclusions.
PortraitMarisa Cristina March is currently a Postdoctoral Research Fellow at the Univeristy of Sussex, and was formerly a postgraduate cosmology student at Imperial College working with Dr Roberto Trotta, in the field of dark energy science.
Untertitel: 2013. Auflage. Previously published in hardcover. Sprache: Englisch.
Erscheinungsdatum: Februar 2015
Seitenanzahl: 200 Seiten