Using AdaBoost Tree to Improve the Efficiency of the Background Rejection Algorithm of HAWC Observatory

The High Altitude Water Cherenkov (HAWC) observatory is a gamma-ray observatory located on the flanks of the Sierra Negra volcano near Puebla, Mexico. The primary goal of HAWC is to measure gamma-rays in the energy range between few hundreds GeV and tens of TeV. However, the HAWC observatory is also sensitive to cosmic-rays, which create a strong background signal for the gamma-ray signal. Removing this background signal is paramount to detect gamma-ray sources. As such, we are investigating the possibilities of using machine learning algorithms to improve the efficiency of the HAWC background rejection algorithm. I will present our progress implementing an AdaBoost decision tree algorithm to simulated HAWC data and ongoing work to test this algorithm on the HAWC real data set.