We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables o
f incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation precision which is consistent with the baseline and discuss possible implications of these results.
A new variant of the effective pomeron exchange model is proposed for the description of the correlation, observed in $pp$ and $pbar{p}$ collisions at center-of-mass energy from SPS to LHC, between mean transverse momentum and charged particles multi
plicity. The model is based on the Regge-Gribov approach. Smooth logarithmic growth with the collision energy was established for the parameter k, the mean rapidity density of charged particles produced by a single string. It was obtained in the model by the fitting of the available experimental data on charged particles rapidity density in $pp$ and $pbar{p}$ collisions. The main effect of the model, a gradual onset of string collectivity with the growth of collision energy, is accounted by a free parameter {beta} that is responsible in an effective way for the string fusion phenomenon. Another free parameter, t, is used to define string tension. We extract parameters {beta} and t from the available experimental results on <pt>-multiplicity correlation at nucleon collision energy $sqrt{s}$ from 17 GeV to 7 TeV. Smooth dependence of both {beta} and t on energy allows to make predictions for the correlation behavior at the collision energy of 14 TeV. The indications to the string interaction effects in high multiplicity events in $pp$ collisions at the LHC energies are also discussed.
