Published April 28, 2014 | Version v1
Thesis Open

Core-collapse Supernova Progenitors in the Era of Untargeted Transient Searches

Authors/Creators

  • 1. Harvard-Smithsonian Center for Astrophysics

Contributors

  • 1. Harvard-Smithsonian Center for Astrophysics
  • 2. San Diego State University

Description

Core-collapse supernovae (SNe) are the highly energetic explosions of massive stars (≳ 8 M☉) that are pervasive in their influence throughout astrophysics. They are the phenomenon with primary responsibility for enriching the universe with many of the heavy elements (like carbon and oxygen) that are needed for life, provide a critical feedback pressure which helps to shape the galaxies that host them, and are the likely formation mechanism for stellar mass black holes. In the past decade, the study of these explosions has been revolutionized by the advent of wide field, untargeted transient searches like Pan-STARRS1 (PS1). These new searches permit the discovery of SNe at unprecedented rates, and absent of many of the selection effects that have enforced biases on past, targeted transient searches. This thesis presents a broad survey of core-collapse SN phenomenology exhibited in the discoveries of untargeted searches, and statistically quantifies population properties of these explosions that link them to distinct classes of progenitor stars. Through studies of the host galaxy and explosion properties of extreme PS1-discovered events, and controlled samples of specific classes of core-collapse objects, we constrain the effect of progenitor star chemical composition (metallicity) on their eventual death states. We provide a new observational, photometric tool which lowers the cost of precisely and accurately measuring the metallicities of distant galaxies and supernova host environments. Moreover, we develop and apply a novel, multi-level Bayesian model for optical transient light curves which we apply to simultaneously interpret more than 20,000 PS1 images. This study illustrates how population-level modeling of data from large photometric surveys can yield improved physical inference on their progenitor stars through comparison to physical models. In the coming era, as next-generation facilities like the Large Synoptic Survey Telescope come online, the supernova discovery rate will accelerate, far outpacing the community's capacity for detailed individual observational follow-up. New observational and statistical tools like those presented here will be critical to enable the next generation of studies in supernova astrophysics.

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