Machine Learning approach to boosting neutral particles identification in the LHCb calorimeter

We present a new approach to identifcation of boosted neutral particles using Electromagnetic Calorimeter (ECAL) of the LHCb detector. The identifcation of photons and neutral pions is currently based on the geometric parameters which characterise the expected shape of energy deposition in the calorimeter. This allows to distinguish single photons in the electromagnetic calorimeter from overlapping photons produced from high momentum π0 decays. The novel approach proposed here is based on applying machine learning techniques to primary calorimeter information, that are energies collected in individual cells around the energy cluster. This method allows to improve separation performance of photons and neutral pions and has no signifcant energy dependence.