The First Full-Scale Prototypes of the Fluorescence detector Array of Single-pixel Telescopes
arXiv:1911.05285 · doi:10.1016/j.astropartphys.2020.102430
Abstract
The Fluorescence detector Array of Single-pixel Telescopes (FAST) is a design concept for a next-generation UHECR observatory, addressing the requirements for a large-area, low-cost detector suitable for measuring the properties of ultra-high energy cosmic rays (UHECRs), having energies exceeding \,eV, with an unprecedented aperture. We have developed a full-scale prototype consisting of four 200\,mm diameter photo-multiplier tubes at the focus of a segmented mirror of 1.6\,m in diameter. In October 2016, September 2017, and September 2018 we installed three such prototypes at the Black Rock Mesa site of the Telescope Array experiment in central Utah, USA. All three telescopes have been steadily taking data since installation. We report on the design and installation of these prototypes, and present some preliminary results, including measurements of artificial light sources, distant ultraviolet lasers, and UHECRs. Furthermore, we discuss some additional uses for these simplified low-cost fluorescence telescopes, including the facilitation of a systematic comparison of the transparency of the atmosphere above the Telescope Array experiment and the Pierre Auger Observatory, a study of the systematic uncertainty associated with the existing fluorescence detectors of these two experiments, and a cross-calibration of their energy and scales.
arXiv comment26 pages, 23 figures
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Cited by in corpus (6)
- Ultra-High-Energy Cosmic Rays: The Intersection of the Cosmic and Energy Frontiers
- GCOS -- The Global Cosmic Ray Observatory
- Science with the Global Cosmic-ray Observatory (GCOS)
- Detecting ultra-high-energy cosmic rays with prototypes of the Fluorescence detector Array of Single-pixel Telescopes (FAST) in both hemispheres
- Reconstruction procedure of the Fluorescence detector Array of Single-pixel Telescopes (FAST)
- Analysis of Fluorescence Telescope Data Using Machine Learning Methods