Searching for Ultra-High Energy Photons applying Convolutional Neural Networks Using the Surface Detector of the Pierre Auger Observatory

Identifying sources of cosmic rays is challenging, as the charged particles are deflected by magnetic fields and do not point back to their sources. Neutral particles, such as ultra-high energy (UHE) γs will point directly to their sources, unless they interact in the interstellar medium or are absorbed. Cosmic ray detectors such as the 3000 km2 surface array of the Pierre Auger Observatory are capable of observing UHE γs above 1018 eV. With increasing energy, their mean free path allows probing extragalactic sources up to a few Mpc. Different methods like BDTs and air-shower universality have been previously applied to the search of γs at different energy ranges. Although no UHE γs have been found, the obtained bounds of the fluxes provide crucial constraints on cosmic-ray acceleration models. Neural networks have the potential to improve discrimination, enhancing the sensitivity to even lower fluxes. In this work, we present a convolutional neural network designed to distinguish between simulated UHE photon and proton showers. We evaluate possible systematics due to the imperfect simulation of air showers and detector effects using an independent test set and a burn sample consisting of 2% of the available data. ... mehrSteps for a future unblinding of the search sample are discussed.