First Detection of Extensive Air Showers Using a Small-Aperture Fluorescence Telescope

TL;DR

This paper demonstrates the first detection of extensive air showers using a small, high-altitude fluorescence telescope, employing both traditional and deep learning methods for event identification.

Contribution

It introduces a compact fluorescence telescope capable of detecting EAS and validates its effectiveness with dual analysis pipelines, including neural networks.

Findings

Successfully detected over 15 EAS events with a small-aperture FT.

Implemented two independent event selection methods: cut-based and deep learning.

Proved the viability of compact fluorescence telescopes for future cosmic ray detection.

Abstract

We report on the successful detection of extensive air showers (EAS) generated by ultra-high-energy cosmic rays using a small-aperture fluorescence telescope (FT) deployed at the Mount Aragats high-altitude research station. The instrument is equipped with a 25 cm diameter Fresnel lens and operates with a 2.625 $\mu$s time resolution. To our knowledge, this represents the first-ever observation of EAS achieved with an FT of such a compact aperture. To isolate shower events from the observational data, we implemented two independent event selection pipelines: a conventional cut-based analysis and a deep learning approach utilizing neural networks. Both algorithms successfully identified over 15 high-confidence EAS tracks from data acquired during clear, moonless nights. We present selected event topologies and detail the background rejection methodology employed to discriminate true shower tracks from spurious focal-plane signals mimicking EAS signatures. These results provide an important proof-of-concept for the advancement of fluorescence detection techniques, demonstrating their viability for forthcoming ground-based and space-borne missions. Future efforts will focus on primary energy reconstruction utilizing a previously developed neural-network framework.