Connections Between the Spectroscopy and Photometry of Type Ia Supernovae
Abstract
Type Ia supernovae (SNe Ia) play a vital role in the study of many topics of astrophysics. They
act as cosmological probes through their well-established nature as precise standardizable candles.
This nature can be described empirically by the systematic relationship between the width of their
light-curve and their peak luminosity, otherwise known as the Phillips relation. As extragalactic
distance indicators, SNe Ia aid in constraining the nature of dark energy. SNe Ia also enrich the
universe with iron-group products, and provide insight into our understanding of stellar evolution as
a whole.
To fully and precisely take advantage of the standardizable properties of SNe Ia, their time
evolution must be fully understood, including the nature of their origin. Unfortunately, the exact
progenitor systems of SNe Ia are still uncertain and are a popular topic in the study of SNe Ia. The
different theorized progenitor scenarios and explosion mechanisms that SNe Ia undergo stem from
the diversity of their observable properties. Understanding the causes of the variation seen between
SNe Ia is imperative in minimizing the Hubble residual, which is used to deduce cosmological
parameters such as the Hubble constant.
Due to the complexities of supernova physics, the variation observed in SNe Ia are often
described using empirical models. Recent advances in statistical and machine-learning techniques
have led to models that provide useful constraints on theories that link observables to the underlying
progenitor system. Although the time-dependent nature of SNe Ia leads to difficulty in obtaining
large samples in order to treat observations statistically, recent surveys of astronomical transients
have led to sample sizes large enough such that modern machine-learning techniques can be applied.
The goal of this thesis is to use this increase in data available to create empirical models that
will aid in the classification and prediction of SN Ia properties, which will further constrain the
progenitor problem. Specifically, these models will be created using a combination of both observed
spectroscopy and photometry. In doing so, correlations between the two regimes are found which
are useful for constraining spectroscopic models and improving light-curve models of SNe Ia.
This thesis is organized in the following manner: Chapter 1 reviews the fundamental nature of
SNe Ia along with previously theorized progenitor systems and explosion mechanisms; Chapter 2
discusses the observational diversity of SNe Ia, including some of the relevant subtypes of SNe Ia
and the observational data used in empirical models created here; Chapter 3 details a cluster analysis
of a sample of SNe Ia that leads to a robust model for classifying SNe Ia; Chapter 4 reviews
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the light-curve-fitter SNooPy and, using SNooPy, illustrates the limitations of the color-stretch
parameter sBV in the near-infrared; Chapter 5 discusses a technique using principal component
analysis to extrapolate NIR spectra of SNe Ia using optical spectroscopy; Chapter 6 proposes a
method of modeling residuals from aforementioned extrapolations to more thoughtfully provide
correlated uncertainties for the application of light-curve calculations; Chapter 7 studies the principal
components from the extrapolation model in Chapter 5 in more detail, and relates them back to
the classification scheme defined in Chapter 3; finally, Chapter 8 provides a summary of the most
notable conclusions in this work.
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- OU - Dissertations [9425]
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