12-20 July 2017
Asia/Seoul timezone
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BEXCO - Room F(201/202/203/204)

[GA200] Characterization of a Maximum Likelihood Gamma-Ray Reconstruction Algorithm for VERITAS



Primary authors


We characterize the improved angular and energy resolution of a new likelihood gamma-ray reconstruction for VERITAS.  The algorithm uses the average photoelectrons stored in templates that are based simulations of large numbers of showers as a function of 5 gamma-ray parameters: energy, zenith angle, core location (x,y), and depth of first interaction in the atmosphere.  Comparing the template predictions of the average photoelectrons in each pixel to observed photoelectrons allows us to calculate the likelihood.  By maximizing the likelihood, we find the optimal gamma-ray parameters.  The maximum likelihood reconstruction improves on the standard analysis which relies on the weighted average of the axis of elongation in the images to determine the gamma-ray direction and look-up tables to relate the observed the energy to the gamma-ray energy. The maximum likelihood approach is unbiased by missing pixel information due to the edge of the camera, pixel cleaning, and statistical fluctuations and is therefore more a more accurate estimator. The drawback is that it takes more CPU time (11 ms per event).