Enhancing event reconstruction for $\gamma$-ray particle detector arrays using transformers
Gamma-ray astronomy from hundreds of GeV to PeV is confined to ground-based experiments that detect air showers induced by $\gamma$-rays entering Earth's atmosphere. While particle detector arrays feature huge detection areas, accurately reconstructing the primary particle properties is difficult due to the sparse sampling of the air shower and its intrinsic fluctuations. In this work, using simulations of a future water-Cherenkov array, we investigate two end-to-end deep learning approaches based on the transformer architecture with different computational complexities that utilize calibrated raw data. We benchmark both methods against well-established methods in the field in terms of $\gamma$-hadron separation, angular, core, and energy reconstruction. Our results show significant improvements across the whole energy range, particularly at low and intermediate energies. This work is the first to consistently demonstrate improved performance in both event reconstruction and $\gamma$-hadron separation using a single architecture.