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A Neural Network based Background Supression Technique applied to VHE Gamma Ray Data coming from the Crab Pulsar
Document Type: Open Access
Degree Program
Physics
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2008
Month Degree Awarded
May
Primary Subject Category
Astrophysics
Secondary Subject Category
Astronomy; Particle physics; Physics
Keywords
Crab Pulsar, Experimental Very High Energy Physics, Gamma Ray Physics, Neural Network Background Supression, Imaging Atmospheric Cherenkov Technique, Flux Upper Limit
Advisor(s) or Committee Chair
Blaylock, Guy
Abstract
In this thesis we present new results for the 99.9% confidence level flux upper limits on the pulsed VHE gamma ray signal coming from the Crab pulsar. In order to achieve optimum hadronic background suppression we implement a new neural network based selection technique and apply it to Cherenkov shower imaging data from the WHIPPLE 10m IACT telescope at Mount Hopkins Arizona. Special emphasis will be given to the fact that the neural network selector is trained with real data exclusively. An energy estimator for gamma ray induced extensive air shower events has been derived from Monte Carlo simulations using the Monte Carlo framework GrISU. This estimator, applied to the image data, serves as input to the neural set selector and is needed to determine the energy dependent flux upper limits. We compare our results to the results from previous studies and the performance of our neural network selection technique to the so-called Supercuts and Optimized Supercuts methods.The new flux upper limits and the new technique show the potential to settle the question about the production mechanism of pulsar radiation. However, the current analysis does not answer this question fully.
Recommended Citation
Reuschle, Christian A., "A Neural Network based Background Supression Technique applied to VHE Gamma Ray Data coming from the Crab Pulsar" (2008). Masters Theses. Paper 145.
http://scholarworks.umass.edu/theses/145
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