Related papers: Modeling NNLO jet corrections with neural networks

We perform a detailed comparison of next-to-next-to-leading order (NNLO) QCD predictions for the W+jet and Z+jet processes with 7 TeV experimental data from ATLAS and CMS. We observe excellent agreement between theory and data for most…

We study the possibility to employ neural networks to simulate jet clustering procedures in high energy hadron-hadron collisions. We concentrate our analysis on the Fermilab Tevatron energy and on the $k_\bot$ algorithm. We consider both…

This talk discusses recent results for next-to-next-to-leading order (NNLO) QCD corrections to jet cross sections and transverse momentum distributions. The results are obtained in the NNLOJET code framework, which provides an…

Precise representation of large-scale undirected network is the basis for understanding relations within a massive entity set. The undirected network representation task can be efficiently addressed by a symmetry non-negative latent factor…

We discuss the next-to-next-to-leading order (NNLO) QCD corrections to Z boson production in association with a jet including all partonic channels at all color levels and including the leptonic decay of the Z boson. We focus on the…

In this talk we present the calculation of next-to-next-to-leading order (NNLO) QCD corrections to dijet production and related observables at hadron colliders in the purely gluonic channel. Results for this channel are obtained keeping all…

In this article we illustrate how event weights for jet events can be calculated efficiently at next-to-leading order (NLO) accuracy in QCD. This is a crucial prerequisite for the application of the Matrix Element Method in NLO. We modify…

The analysis of event shape variables enable studies of QCD and determinations of alpha-s, but require predictions to next-to-leading order. The predictions of two next-to-leading order programs are compared for current jet thrust, current…

We study the efficiency of a neural-net filter and deconvolution method for estimating jet energies and spectra in high-background reactions such as nuclear collisions at the relativistic heavy-ion collider and the large hadron collider.…

We present the computation of next-to-leading order (NLO) QCD corrections to di-photon production in association with two or three hard jets in pp collisions at a center-of-mass energy of 8 TeV. The inclusion of NLO corrections is shown to…

We compute the next-to-next-to-leading order (NNLO) QCD corrections to event shape distributions and their mean values in deep inelastic lepton-nucleon scattering. The magnitude and shape of the corrections varies considerably between…

Network structure is growing popular for capturing the intrinsic relationship between large-scale variables. In the paper we propose to improve the estimation accuracy for large-dimensional factor model when a network structure between…

We discuss the motivation for making predictions for jet cross sections at next-to-next-to-leading order. We describe the theoretical ingredients needed for such a calculation and briefly review the progress in the field.

Machine learning has played a pivotal role in advancing physics, with deep learning notably contributing to solving complex classification problems such as jet tagging in the field of jet physics. In this experiment, we aim to harness the…

This paper presents an implementation of multilayer feed forward neural networks (NN) to optimize CMOS analog circuits. For modeling and design recently neural network computational modules have got acceptance as an unorthodox and useful…

Traditionally, deriving aerodynamic parameters for an airfoil via Computational Fluid Dynamics requires significant time and effort. However, recent approaches employ neural networks to replace this process, it still grapples with…

Sequence-based modeling broadly refers to algorithms that act on data that is represented as an ordered set of input elements. In particular, Machine Learning algorithms with sequences as inputs have seen successfull applications to…

The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work. The primary objective is to develop suitable CNN architecture for variable flow conditions and object geometry,…

I review progress related to the calculation of QCD jet cross sections at the NLO accuracy. After a short introduction into the theory of NLO calculations, I discuss two recent developments: the calculation of two- and three-jet…

Computing optimal transport (OT) for general high-dimensional data has been a long-standing challenge. Despite much progress, most of the efforts including neural network methods have been focused on the static formulation of the OT…