Simulations play an important role in high energy physics. They help to optimize detectors for experiments. We present a neural-network-based algorithm for prediction of parameters of secondaries in a hadronic shower using calorimetric observables. The method proposed was tested for two hadronic models of the Geant4 package.
Sergey Korpachev Marina Chadeeva (LPI)