Real-time discrimination of photon pairs using machine learning at the LHC

ALP–mediated decays and other as-yet unobserved B decays to di-photon final states are a challenge to select in hadron collider environments due to the large backgrounds that come directly from the pp collision. We present the strategy implemented by the LHCb experiment in 2018 to efficiently select such photon pairs. A fast neural network topology, implemented in the LHCb real-time selection framework achieves high efficiency across a mass range of 4–20 GeV/c². We discuss implications and future prospects for the LHCb experiment.