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.
Highlights
Machine learning and, in particular, deep learning using neural networks (NN) is a field which is currently beginning to thrive in astronomy
This means we can make use of well-established techniques to tackle the challenge of both self-triggering on radio signals, preferably on the single antenna level, and performing online parameter-reconstruction of neutrino and ultra high energy cosmic ray (UHECR) induced air-shower events based on detected signals in an array of antennas
A detailed comparison of the two trigger algorithms is beyond the scope of this proceeding, we postpone this together with an accurate calculation of the expected data stream to a future publication
Summary
In particular, deep learning using neural networks (NN) is a field which is currently beginning to thrive in astronomy. This means we can make use of well-established techniques to tackle the challenge of both self-triggering on radio signals, preferably on the single antenna level, and performing online parameter-reconstruction of neutrino and ultra high energy cosmic ray (UHECR) induced air-shower events based on detected signals in an array of antennas. The feasibility of self-triggering on air-shower events via the measurement of the radio signals is reinforced by the reports of [2, 3] The purpose of this proceeding is to demonstrate the feasibility of NN based triggering and to motivate further research in this direction
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