Identification of air-shower radio pulses for the GRAND online trigger
August 08, 2023
The Grand Radio Array for Neutrino Detection (GRAND) is an envisioned observatory that aims to detect the radio emission from air showers induced by ultra-high-energy cosmic particles; in particular, by neutrinos. Because these are rare, GRAND requires a large detection area, necessitating the use of inexpensive radio-detection units that must trigger autonomously. Such a trigger must achieve a high rejection efficiency of the dominant transient radio background, while keeping a high detection efficiency for air shower radio pulses. Fortunately, air shower simulations and field data suggest that air shower radio pulses exhibit characteristic features whose exploitation would lead to a powerful background rejection. We present the results of a machine learning signal classification method that has been tested on simulations and data recorded by a GRAND prototype in the Gansu province of China. Considering time traces that pass a simple $3\sigma$-transients pre-trigger, a neural network is able to keep 66% of the air showers pulses for a Signal to Noise Ratio (SNR) between 3 and 4, and more than 86% after a SNR of 4, while rejecting between 97% and 99% of the background traces. This trigger method will eventually be implemented to the next prototypes to be tested under field conditions.
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