Title: Exploring deep learning as an event classification method for the Cherenkov Telescope Array,
Abstract: Telescopes based on the imaging atmospheric Cherenkov technique (IACTs)
detect images of the atmospheric showers generated by gamma rays and cosmic
rays as they are absorbed by the atmosphere. The much more frequent cosmic-ray
events form the main background when looking for gamma-ray sources, and
therefore IACT sensitivity is significantly driven by the capability to
distinguish between these two types of events. Supervised learning algorithms,
like random forests and boosted decision trees, have been shown to effectively
classify IACT events. In this contribution we present results from exploratory
work using deep learning as an event classification method for the Cherenkov
Telescope Array (CTA). CTA, conceived as an array of tens of IACTs, is an
international project for a next-generation ground-based gamma-ray observatory,
aiming to improve on the sensitivity of current-generation experiments by an
order of magnitude and provide energy coverage from 20 GeV to more than 300
TeV.