Offline Signal Identification with GRANDProto300

TL;DR

This paper evaluates classical signal identification methods for the GRANDProto300 array, a prototype for neutrino detection, focusing on detecting cosmic-ray air showers amid noise using simulations and real background data.

Contribution

It demonstrates the effectiveness of classical approaches for offline signal identification in the GRANDProto300 array using realistic simulations and measured background data.

Findings

High efficiency in signal identification demonstrated

Classical methods validated against real background data

Potential for autonomous radio detection techniques

Abstract

The GRANDProto300 (GP300) array is a pathfinder for the Giant Radio Array for Neutrino Detection (GRAND) project. Serving as a test bench, the GP300 array is expected to pioneer techniques of autonomous radio detection including identification and reconstruction of nearly horizontal cosmic-ray (CR) air showers, and shed light in understanding the interesting `transition region' from the galactic to extragalactic CR sources. An offline analysis of signal identification over ambient noise is crucial at this stage, where very relaxed self-triggering thresholds of radio antennas will be used for study purposes. In this work, we show results and efficiency of signal identification with classical approaches using a wide set of simulated realistic signal templates and also validated against measured background recorded by deployed prototypes.