  The paper describes a novel neural-network-based approach to study the
distributions of secondaries produced in hadronic showers using observables
provided by highly granular calorimeters. The response is analysed of the
highly granular scintillator-steel hadron calorimeter to negative pions with
momenta from 10 to 80 GeV simulated with two physics lists from the Geant4
package version 10.3. Several global observables, which characterise different
aspects of hadronic shower development, are used as inputs for a deep neural
network. The network regression model is trained using a supervised learning
and exploiting true information from the simulations. The trained model is
applied to predict a number of neutrons and energy of neutral pions produced
within a hadronic shower. The achieved performance and possible application of
the model to validation of simulations are discussed.
