Modelling NNLO jet corrections with neural networks

We present a preliminary strategy for modeling multidimensional distributions through neural networks. We study the efficiency of the proposed strategy by considering as input data the two-dimensional next-to-next leading order (NNLO) jet kappa-factors distribution for the ATLAS 7 TeV 2011 data. We then validate the neural network model in terms of interpolation and prediction quality by comparing its results to alternative models.