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

TL;DR

This paper presents a neural network-based approach for real-time detection and parameter reconstruction of air-shower events using radio signals from single antennas, aiming to improve trigger accuracy and reduce false positives.

Contribution

It introduces a novel neural network method for online triggering and reconstruction of air-shower events at the single-antenna level, enhancing detection efficiency.

Findings

Effective discrimination between signal and background events.

Successful online reconstruction of shower parameters.

Potential for real-time application in radio detection arrays.

Abstract

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.