Towards online triggering for the radio detection of air showers using deep neural networks
The detection of air-shower events via radio signals requires to develop a trigger algorithm for a 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 as well as performing an online reconstruction of the shower parameters using neural networks.
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Categories
Instrumentation and Methods for Astrophysics
High Energy Physics - Experiment
Data Analysis, Statistics and Probability
