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The paper describes a novel neural-network-based approach to study thedistributions of secondaries produced in hadronic showers using observablesprovided by highly granular calorimeters. The response is analysed of thehighly granular scintillator-steel hadron calorimeter to negative pions withmomenta from 10 to 80 GeV simulated with two physics lists from the Geant4package version 10.3. Several global observables, which characterise differentaspects of hadronic shower development, are used as inputs for a deep neuralnetwork. The network regression model is trained using a supervised learningand exploiting true information from the simulations. The trained model isapplied to predict a number of neutrons and energy of neutral pions producedwithin a hadronic shower. The achieved performance and possible application ofthe model to validation of simulations are discussed.

Machine-learning-based prediction of parameters of secondaries in hadronic showers using calorimetric observables