Application of a maximum-likelihood classifier in TeV gamma-ray astronomy

Abstract

A maximum-likelihood classifier has been applied to the problem of hadronic background rejection in TeV gamma-ray astronomy. Using data taken during observations of the Crab Nebula by the Whipple Observatory 10-m imaging camera, the classifier achieves a background rejection bettered only by neural network and 'Supercuts' rejection techniques. Used in conjunction with these two rejection techniques it attains a level of rejection capability unsurpassed by any analysis technique yet applied to the Crab Nebula database.