# Modeling NNLO jet corrections with neural networks

## TL;DR

This paper proposes a neural network-based method to model complex multidimensional jet correction distributions, demonstrating its effectiveness on NNLO jet k-factors from ATLAS data.

## Contribution

It introduces a novel neural network approach for modeling multidimensional distributions in high-energy physics, validated on NNLO jet correction data.

## Key findings

- Neural network accurately interpolates NNLO jet k-factors
- Model outperforms alternative approaches in prediction quality
- Effective for multidimensional distribution modeling

## 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.