  In particle physics, Monte Carlo (MC) event generators are needed to compare
theory to the measured data. Many MC samples have to be generated to account
for theoretical systematic uncertainties, at a significant computational cost.
Therefore, the MC statistic becomes a limiting factor for most measurements and
the significant computational cost of these programs a bottleneck in most
physics analyses. In this contribution, the Deep neural network using
Classification for Tuning and Reweighting (DCTR) approach is evaluated for the
reweighting of two systematic uncertainties in MC simulations of top quark pair
production within the CMS experiment. DCTR is a method, based on a Deep Neural
Network (DNN) technique, to reweight simulations to different model parameters
by using the full kinematic information in the event. This methodology avoids
the need for simulating the detector response multiple times by incorporating
the relevant variations in a single sample.
