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Improving Cosmological Distance Measurements with Type Ia Supernovae: From Pixels to Dark Energy

  • Author(s): Boone, Kyle Robert
  • Advisor(s): Perlmutter, Saul
  • et al.
Abstract

In the late 1990s, precise distance measurements with Type Ia supernovae (SNe Ia) were used to show that the expansion of the universe is accelerating. One possibility is that this accelerated expansion is due to an additional form of energy referred to as “dark energy” which contributes roughly 70% of the total energy in the present day universe. The properties of dark energy are not currently well-constrained, and a wide range of different cosmological probes are currently being designed to explore the fundamental nature of the accelerated expansion of the universe. SNe Ia have remained one of the strongest cosmological probes, and upcoming experiments such as the Large Synoptic Survey Telescope (LSST) are expected to discover over 100,000 SNe Ia that can be used for cosmology. The uncertainties on cosmological parameters derived from these large samples of SNe Ia will be entirely dominated by the systematic uncertainties of distance measurements to SNe Ia. In this dissertation, we discuss several different methods of improving the systematic uncertainties in distance measurements to SNe Ia.

This dissertation is split into three main chapters each discussing how to improve a different aspect of distance measurements to SNe Ia. In Chapter 2, we examine how instrumental calibration can affect these distance measurements, and discuss a new anomalous behaviour of CCD readout electronics related to the binary encoding of pixel values that affects most astronomical instruments currently in use. For the Nearby Supernova Factory, this anomaly introduces a dispersion in the measured B-band/U-band magnitudes of 0.11 mag/0.51 mag for the faintest 20% of measurements.

Another major source of systematic uncertainty in distance measurements to SNe Ia is intrinsic variation of the SNe Ia. In Chapter 3, we develop a new method of parametrizing SNe Ia using manifold learning to generate a non-linear decomposition of the intrinsic diversity of their spectra near maximum light. We identify regions of the parameter space of SNe Ia where previous standardization methods such as SALT2 have biases of up to 0.3 mag, and show how correlations between host galaxy properties and distance estimates are greatly reduced when standardizing SNe Ia using our new parametrization.

Finally, in Chapter 4, we discuss how upcoming surveys such as LSST will need to rely on photometric classification to identify the majority of the transients that they discover, which means that samples of SNe Ia used for cosmology will be contaminated with other types of transients. We developed a set of techniques for photometric classification to address the fact that spectroscopic subsamples used for training classifiers are typically highly biased compared to the full samples of transients and variables that will be discovered. Using these techniques, we built a photometric classifier that won the PLAsTiCC photometric classification challenge out of 1,094 competing teams.

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