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.