Towards online triggering for the radio detection of air showers using deep neural networks

Abstract

The detection of air-shower events via radio signals requires the development of a trigger algorithm for clean discrimination between signal and background events in order to reduce the data stream coming from false triggers. In this contribution we will describe an approach to trigger air-shower events on a single-antenna level aswell as performing an online reconstruction of the shower parameters using neural networks.