  The precise reconstruction of properties of photons and electrons in modern
high energy physics detectors, such as the CMS or Atlas experiments, plays a
crucial role in numerous physics results. Conventional geometrical algorithms
are used to reconstruct the energy and position of these particles from the
showers they induce in the electromagnetic calorimeter. Despite their accuracy
and efficiency, these methods still suffer from several limitations, such as
low-energy background and limited capacity to reconstruct close-by particles.
This paper introduces an innovative machine-learning technique to measure the
energy and position of photons and electrons based on convolutional and graph
neural networks, taking the geometry of the CMS electromagnetic calorimeter as
an example. The developed network demonstrates a significant improvement in
resolution both for photon energy and position predictions compared to the
algorithm used in CMS. Notably, one of the main advantages of this new approach
is its ability to better distinguish between multiple close-by electromagnetic
showers.
