In this thesis, we discuss the optimization procedure for the TAUWER (TAU shoWER) experiment, which is designed to detect showers generated by Earth-skimming neutrinos. Monte Carlo Simulations are done through CORSIKA (COsmic Ray SImulations for KAscade) software to provide us with detailed information about hit patterns on the detector array from these showers. We use this to determine the trigger conditions, rates, and optimal detector layout. We also use machine learning classification methods to generate classifiers to assign the energy scale for observed showers.
| Identifer | oai:union.ndltd.org:cmu.edu/oai:repository.cmu.edu:dissertations-1440 |
| Date | 01 May 2014 |
| Creators | Tang, Zhen |
| Publisher | Research Showcase @ CMU |
| Source Sets | Carnegie Mellon University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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