  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 on 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 VERITAS analysis which
relies on: 1. the weighted average of the axis of elongation in the images to
determine the gamma-ray direction and 2. look-up tables that relate the
observed energy deposition of Cherenkov photons to the true gamma-ray energy.
Not only is the maximum likelihood method more accurate, but it is also not
biased by missing pixel information due to the edge of the camera or pixel
cleaning. The drawback is that it takes more CPU time (80 ms/event).
