Currently in the design stage, the Cherenkov Telescope Array (CTA) is an advanced facility for ground-based high-energy gamma-ray astronomy. This research presents the projected system performance of CTA calculated as part of a large Monte Carlo simulation effort including air shower and telescope response simulations. The integral sensitivity of CTA’s baseline Subarray-E is found to be nearly an order of magnitude more sensitive between 100 GeV and 10 TeV compared to existing ground-based Cherenkov telescope systems. This research finds that this particular subarray achieves CTA’s goal of milli-Crab sensitivity at 1 TeV. In addition, this work uses a multi-layer perceptron neural network to separate the cosmic-ray background from the gamma-ray signal. This includes employing 5 different methods for estimating the neural network response cut, with the energy-scaled signifi- cance method being found to provide the best and most stable performance. Performance measures calculated include: energy resolution, angular resolution, effective collecting area as well as flux sensitivity. In addition, the results of three small studies are also presented. The first includes the performance results of a high-altitude (3700 m) subarray. This re- search confirms a gain in sensitivity at the lowest detectable energies under ∼100 GeV. The second and third are small technical studies on CTA’s dynamic range performance. Finally, to assess CTA’s prospects for detecting pulsed gamma-ray emission data for the Crab and Vela pulsars taken from Fermi Gamma-Ray Space Telescope observations are analysed and compared with the CTA sensitivity performance derived in this research. Results for CTA Subarray-E and Subarray-B suggest these arrays will both have sufficient sensitivity perfor- mance for detecting the Vela pulsar if it behaves as expected on the basis of the published spectra.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:552952 |
| Date | January 2012 |
| Creators | Rulten, Cameron Boyd |
| Publisher | Durham University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.dur.ac.uk/3632/ |
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