  In high-energy particle physics, complex Monte Carlo (MC) simulations are
needed to compare theory predictions to measurable quantities. Many and large
MC samples are needed to be generated to take into account all the systematics.
Therefore, the MC statistics (and hence the MC modeling uncertainties) become a
limiting factor for most measurements. Moreover, the significant computational
cost of these programs becomes a bottleneck in most physics analyses.
Therefore, it is extremely important to find a way to reduce the MC samples
generated to decrease the MC statistical uncertainties and lower the
computational cost. In these proceedings, we evaluate an approach called Deep
neural network using Classification for Tuning and Reweighting (DCTR). DCTR is
a method based on a Deep Neural Network (DNN) to reweight simulations to
different models or model parameters and fit simulations, using the full
kinematic information in the event. This reweighting methodology avoids the
need for simulating the detector response multiple times by incorporating the
relevant variations in a single sample. In this way, the MC statistical
uncertainties and the computational cost are both reduced. Moreover, unlike the
standard reweighting, in which the ratio in bins of two histograms at truth
level is performed, multidimensional and unbinned information can be used as
inputs to the DNN. In addition, DCTR can perform tasks that are not possible
with other current existing methods, such as continuous reweighting as a
function of any MC parameter, simultaneous reweighting of more MC parameters
and tuning MC simulations to the data. We test the method on MC simulations of
top quark pair production, which we reweight to different SM parameter values
and to different QCD models.
