Advanced Statistical Methods for Astrophysical Probes of Cosmology

by Marisa Cristina March

Overview

This 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.

Product Details

ISBN-13:	9783642350597
Publisher:	Springer Berlin Heidelberg
Publication date:	01/12/2013
Series:	Springer Theses
Edition description:	2013
Pages:	180
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

About the Author

Marisa 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.

Table of Contents

Introduction.- Cosmology background.- Dark energy and apparent late time acceleration.- Supernovae Ia.- Statistical techniques.- 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.- Index.