Advancements in Autonomous Detection for Cosmic-Ray Observations

The recent paper titled "Development of an Autonomous Detection-Unit Self-Trigger for GRAND" by Pablo Correa and colleagues presents advancements in self-triggering techniques for the Giant Radio Array for Neutrino Detection (GRAND). The study addresses a significant challenge in the radio detection of extensive air showers, which is the need for an autonomous radio self-trigger.

The authors describe two first-level trigger (FLT) methods: one based on a template-fitting algorithm and another utilizing a convolutional neural network (CNN). These methods were evaluated for their performance in signal selection efficiency and background rejection efficiency. The findings indicate that both methods can reject over 40% of background noise while maintaining a signal selection efficiency of 90% at the 5σ level.

This development is crucial for enhancing the sensitivity and accuracy of cosmic-ray detection, particularly in distinguishing genuine air shower signals from background noise. The implications of this research extend to improving the overall effectiveness of the GRAND project, which aims to detect ultra-high-energy cosmic rays and neutrinos. The ability to autonomously trigger detections could lead to more reliable data collection and analysis in future cosmic-ray observatories.

The paper was presented at the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA2024) and contributes to ongoing efforts in the field of astrophysics to refine detection methods for high-energy particles. For further details, the paper can be accessed through arXiv:2409.01026.