BEXCO - Room F(201/202/203/204)
[GA045] Gamma/hadron separation with multivariate analysis methods for the LHAASO-WCDA experiment
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 project, 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 hadronic cosmic rays. Rather than a single key parameter for Gamma/hadron descrimination, 4 sensitive parameters are chosen for purpose of further improving the detector sensitivity, using some multivariate analysis methods such as the artificial neural network and the boosting decision tree. By analyzing the simulation data, these two multivariate analysis algorithms both manifest excellent Gamma/hadron separation powers, and greatly improve the quality factor and the sensitivity of the WCDA at the low energy end (<1 TeV).