  Pions constitute nearly $70\%$ of final state particles in ultra high energy
collisions. They act as a probe to understand the statistical properties of
Quantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in
such relativistic heavy ion collisions (HIC). Apart from this, direct photons
are the most versatile tools to study relativistic HIC. They are produced, by
various mechanisms, during the entire space-time history of the strongly
interacting system. Direct photons provide measure of jet-quenching when
compared with other quark or gluon jets. The $\pi^{0}$ decay into two photons
make the identification of non-correlated gamma coming from another process
cumbersome in the Electromagnetic Calorimeter. We investigate the use of deep
learning architecture for reconstruction and identification of single as well
as multi particles showers produced in calorimeter by particles created in high
energy collisions. We utilize the data of electromagnetic shower at calorimeter
cell-level to train the network and show improvements for identification and
characterization. These networks are fast and computationally inexpensive for
particle shower identification and reconstruction for current and future
experiments at particle colliders.
