Supernova Cosmology and How to Talk about It : New Approaches to Cosmological Parameter Inference with Type Ia Supernovae and an Assessment of the Education and Public Outreach Program of the Dark Energy Survey / Rachel Cane Wolf.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Wolf, Rachel Cane, author.

Contributor:

Sako, Masao, degree supervisor.

Aguirre, James, degree committee member.

Bernstein, Gary, degree committee member.

Lidz, Adam, degree committee member.

Thomson, Evelyn, degree committee member.

University of Pennsylvania. Department of Physics and Astronomy, degree granting institution.

Language:

English

Subjects (All):

Penn dissertations--Physics and Astronomy.

Physics and Astronomy--Penn dissertations.

Local Subjects:

Penn dissertations--Physics and Astronomy.

Physics and Astronomy--Penn dissertations.

Physical Description:

xxii, 279 leaves : illustrations ; 29 cm

Production:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2017.

Summary:

The discovery of the accelerating expansion of the Universe launched a new chapter in modern cosmology. Evidence for this accelerating expansion was first observed using Type Ia supernovae, which are brilliant, standardizable explosions that can be detected at large distances and used to infer cosmological parameters. New surveys are being designed to detect thousands of Type Ia supernovae, ushering in an era where parameter inference is no longer limited by statistics, but by systematic uncertainties. One of these systematics which is not well understood is the progenitor and progenitor environment, which can be investigated by studying properties of the supernova host galaxy. In this dissertation, I use the three-year sample of photometrically-classified and spectroscopically-confirmed Type Ia supernovae from Sloan Digital Sky Survey-II Supernova Survey to explore correlations between supernova luminosity and host-galaxy mass, metallicity, and star-formation rate. Observations suggest that such correlations should be incorporated to improve the standardization of Type Ia supernova luminosities. As such, new techniques for parameter inference will need to accommodate increasingly large samples of supernovae and a variety of standardization models. In this dissertation, I also introduce the BAyesian hierarchical Modeling with BIased Simulations (BAMBIS) algorithm, a novel approach to parameter inference using Type Ia supernovae which can, in principle, include systematics such as host-galaxy correlations in a robust statistical framework.In addition to offering new scientific research opportunities, the quest to understand the evolution of the cosmos brings excellent opportunities for astronomers to engage in science education and public outreach (EPO). I present an analysis of the Dark Energy Survey EPO program, a unique large-scale astronomy EPO initiative organized and led entirely by professional astronomers. In this analysis, I detail the development of the EPO program as well as analyze the strengths and weaknesses of a subset of specific initiatives. I also discuss scientists' reported methods of communicating science with the public.

Notes:

Ph. D. University of Pennsylvania 2017.

Department: Physics and Astronomy.

Supervisor: Masao Sako.

Includes bibliographical references.

OCLC:

1334673812