Modeling NNLO jet corrections with neural networks
arXiv:1704.00471 · doi:10.5506/APhysPolB.48.947
Abstract
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 k-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.
arXiv commentProceedings for the Cracow Epiphany Conference 2017, final version
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