  We present a new approach to identification of boosted neutral particles
using Electromagnetic Calorimeter (ECAL) of the LHCb detector. The
identification 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 $\pi^0$
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 significant
energy dependence.
