M. Zotov, A. Trusov, P. Klimov, K. Asatryan, A. Belov, G. Gabaryan, V. Kudryavtsev, A. Murashov

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 $μ$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.