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Telescope: Earth

  • Author(s): Albin, Eric Kenneth
  • Advisor(s): Whiteson, Daniel O
  • et al.
Creative Commons 'BY-SA' version 4.0 license
Abstract

Until the construction of the aptly-named cosmotron in the early 1950s, particle physicists relied on cosmic ray tracks in photographic emulsions and cloud chambers to discover antimatter and subatomic particles. Nearly 110 years since their discovery, the origin and composition of the highest energy cosmic rays remains largely a complete mystery.

In that time, solid-state pixel technology has become a mainstay in both particle detectors and consumer smartphone cameras, but for largely economic reasons, modern cosmic ray surface detectors are primarily water-Cherenkov or plastic-scintillator type. However, with both the worldwide number of smartphone users exceeding 3 billion and at least as many laptop computers in use, consumer solid-state pixel sensors (cameras) have a combined surface area over 5 times the cross-sectional area of the Pierre Auger Observatory's 1,660 water-Cherenkov detectors.

In this dissertation, I discuss the potential, the process and the problems faced in turning the populated planet into a cosmic ray telescope using smartphone cameras. In Chapter 2, I develop novel extensive air shower longitudinal muon and photon density models that clearly exhibit better agreement with CORSIKA simulations than popular alternatives. I also provide a parameterization scheme that spans variations in primary energy, inclination angle, and observation height. In Chapter 4, I identify muon and photon signatures present in real CRAYFIS user data, propose a novel test array of CRAYFIS-enabled smartphones, and present a high-performance data acquisition application. At last, in Chapter 5, I calculate the sensitivity of a global CRAYFIS network to simultaneous extensive air showers as a function of observation time and incident flux, and find that at least 1 million CRAYFIS users worldwide are needed to identify novel phenomena signal over background at 3$\sigma$ statistical significance over a reasonable time-span.

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