Related papers: LHC Hadronic Jet Generation Using Convolutional Va…
The shape of jets produced in quasi-real photon-proton collisions at centre-of-mass energies in the range $134-277$ GeV has been measured using the hadronic energy flow. The measurement was done with the ZEUS detector at HERA. Jets are…
At the Large Hadron Collider (LHC) it will become possible for the first time to investigate experimentally the forward region in hadron-hadron collisions via high-$p_T$ processes. In the LHC forward kinematics QCD logarithmic corrections…
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two novel methods that generate LHC jets as point clouds…
Current state-of-the-art generative approaches frequently rely on a two-stage training procedure, where an autoencoder (often a VAE) first performs dimensionality reduction, followed by training a generative model on the learned latent…
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for…
Unfolding, for example of distortions imparted by detectors, provides suitable and publishable representations of LHC data. Many methods for unbinned and high-dimensional unfolding using machine learning have been proposed, but no…
Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W…
We consider the associated production of Higgs boson and hadronic jet(s) in $pp$ collisions at the LHC for the first time using the $k_T$-factorization approach. Our analysis is based on the off-shell gluon-gluon fusion subprocess, where…
We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate…
A search is presented for new particles produced at the LHC in proton-proton collisions at $\sqrt{s} =$ 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to…
The Monte Carlo event generator HYDJET++ is one of the few generators, designed for the calculations of heavy-ion collisions at ultrarelativistic energies, which combine treatment of soft hydro-like processes with the description of jets…
Collisions of heavy ion nuclei at relativistic speeds (close to the speed of light) creates a high temperature and very dense form of matter, now known to consist of de-confined quarks and gluons, named the quark gluon plasma (QGP). In this…
We present a novel event generation framework for the efficient simulation of vector boson plus multi-jet backgrounds at the high-luminosity LHC and at possible future hadron colliders. MPI parallelization of parton-level and particle-level…
A method of reconstruction of the top quarks produced in the process E+E- -> t\bar{t} -> 6 jets at a Linear Collider (LC) is proposed. The approach does not involve a kinematic fit, as well as assumptions on the invariant masses of the…
Transformers have become the primary architecture for natural language processing. In this study, we explore their use for auto-regressive density estimation in high-energy jet physics, which involves working with a high-dimensional space.…
We present version 2.2 of the High Energy Jets (HEJ) Monte Carlo event generator for hadronic scattering processes at high energies. The new version adds support for two further processes of central phenomenological interest, namely the…
We study the production of jets in hadronic collisions, by computing all contributions proportional to $\alpha_S^n\alpha^m$, with $n+m=2$ and $n+m=3$. These correspond to leading and next-to-leading order results, respectively, for…
Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between…
We develop the first event generator, the electron-Heavy-Ion-Jet-INteraction-Generator (eHIJING), for the jet tomography study of electron-ion collisions. In this generator, energetic jet partons produced from the initial hard scattering…
The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of…