The Large High Altitude Air Shower Observatory (LHAASO) is going
to be built at an altitude of 4410 meters in Daocheng, Sichuan Province,
China. The Water Cherenkov Detector Array (WCDA), one of the major
components of the LHAASO experiment, focuses on surveying the Northern
sky for gamma ray sources in a wide energy range (0.1 to 30 TeV).
One of the main tasks of the data analysis of the WCDA is to suppress
the large number of background events originated from primary cosmic rays.
Rather than a single key parameter for Gamma/Proton discrimination,
4 sensitive parameters are chosen for the purpose of further improving
the detector sensitivity, using some multi-variate analysis methods
such as the artificial neural network and the boosting decision tree.
By analyzing the simulation data, these two multi-variate analysis algorithms
both manifest excellent Gamma/Proton separation powers, and greatly improve
the quality factor and the sensitivity of the WCDA at the low energy.