Modeling NNLO jet corrections with neural networks
High Energy Physics - Phenomenology
2017-08-02 v2
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.
Cite
@article{arxiv.1704.00471,
title = {Modeling NNLO jet corrections with neural networks},
author = {Stefano Carrazza},
journal= {arXiv preprint arXiv:1704.00471},
year = {2017}
}
Comments
Proceedings for the Cracow Epiphany Conference 2017, final version