Related papers: LHC Hadronic Jet Generation Using Convolutional Va…
We study high-energy jet production in the multi-Regge limit making use of the Monte Carlo event generator BFKLex which includes collinear improvements in the form of double-log contributions as presented in [1]. Making use of the anti-kt…
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 calculations of single inclusive jet transverse momentum and energy spectra at forward rapidity ($5.2\!<\!y\!<\!6.6$) in proton-lead collisions with $\sqrt{s_{NN}}$ = 5.02 TeV. The predictions are obtained with the KaTie Monte…
We discuss high-pT production processes at forward rapidities in hadron-hadron collisions, and describe recent results from using QCD high-energy factorization in forward jet production at the LHC.
Differential dijet cross sections are measured in photoproduction in the region of photon virtualities Q^2 < 1 GeV^2 with the H1 detector at the HERA ep collider using an integrated luminosity of 66.6 pb^{-1}. Jets are defined with the…
The Large Hadron Collider (LHC) is the first machine that provides high enough energy to produce large numbers of boosted top quarks. The decay products of these top quarks are confined to a cone in the top quark flight direction and can be…
Multimodal Variational Autoencoders have emerged as a popular tool to extract effective representations from rich multimodal data. However, such models rely on fusion strategies in latent space that destroy the joint statistical structure…
An autoencoder is a self-supervised machine-learning network trained to output a quantity identical to the input. Owing to its structure possessing a bottleneck with a lower dimension, an autoencoder works to achieve data compression,…
The irreducible background from Z(nunu)+jets, to beyond the Standard Model searches at the LHC, can be calibrated using gamma+jets data. The method utilises the fact that at high vector boson pT, the event kinematics are the same for the…
Autoencoders and generative neural network models have recently gained popularity in fluid mechanics due to their spontaneity and low processing time instead of high fidelity CFD simulations. Auto encoders are used as model order reduction…
We demonstrate that a future high-energy electron-proton collider like THERA could largely extend the current HERA program in jet photoproduction of testing QCD and determining the partonic structure of the proton and the photon. Depending…
I describe two methods for studying hadronic backgrounds to prompt photon production with L3, and compare the observed background rates with Monte Carlo predictions. I find that the Monte Carlo models JETSET and HERWIG underestimate the…
We propose a new strategy to probe heavy neutrinos with non-universal fermion couplings at the Large Hadron Collider (LHC) using a novel production mechanism and machine-learning algorithms. Focusing on proton--proton collisions at…
The advent of high-energy hadron colliders necessitates efficient and accurate computation of multi-jet production processes, both as QCD processes in their own right and as backgrounds for other physics. The algorithm that performs these…
This paper presents updated Monte Carlo configurations used to model the production of single electroweak vector bosons (W, Z/$\gamma^{*}$) in association with jets in proton-proton collisions for the ATLAS experiment at the Large Hadron…
Hadronic jets in deeply inelastic electron-proton collisions are produced by the scattering of a parton from the proton with the virtual gauge boson mediating the interaction. The HERA experiments have performed precision measurements of…
For analytical description of the initial stage of jet generation in nonequilibrium inhomogeneous plasma in the magnetohydrodynamic approximation, possible generalizations of solutions of the nonlinear equation for the stream function are…
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential…
We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level…
Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning…