Related papers: Reweighting a parton shower using a neural network…
We present a method to match the multi-parton states generated by the High Energy Jets Monte Carlo with parton showers generated by the Ariadne program using the colour dipole model. The High Energy Jets program already includes a full…
Probability density function (PDF) based turbulent combustion modelling is limited by the need to store multi-dimensional PDF tables that can take up large amounts of memory. A significant saving in storage can be achieved by using various…
Porous materials -- natural or engineered -- often exhibit dual pore-network structures that govern processes such as mineral exploration and hydrocarbon recovery from tight shales. Double porosity/permeability (DPP) mathematical models…
To prepare for the next generation particle collider, likely to be a high-energy precision-frontier electron-positron machine, theoretical predictions must improve in tandem. One aspect in which it is necessary to build on the progress made…
Multi-step time-series prediction is an essential supportive step for decision-makers in several industrial areas. Artificial intelligence techniques, which use a neural network component in various forms, have recently frequently been used…
We specify recursive equations that could be used to generate a lowest order parton shower for hard scattering in hadron-hadron collisions. The formalism is based on the factorization soft and collinear interactions from relatively harder…
We compare fixed order and parton shower matched predictions for the process ${pp\rightarrow \ell^+\nu_{\ell} \ell^-\bar{\nu}_{\ell} \ell^{\pm}{\nu}_{\ell} b\bar{b}+X}$ at NLO in QCD, including the orders $\mathcal{O}(\alpha_s^3\alpha^6)$…
In this work we present an efficient construction of baryon interpolating fields for lattice QCD computations of two and three point functions. These are essential building blocks of computations of nucleon parton distribution functions…
We present algorithms that interleave photon radiation from the final state and the initial state with the QCD evolution in the antenna-based Vincia parton shower. One of the algorithms incorporates the complete soft and collinear structure…
Perturbative quantum chromodynamics (QCD) ceases to be applicable at low interaction energies due to the rapid increase of the strong coupling. In that limit, the non-perturbative regime determines the properties of quarks and gluons…
We formulate PanScales parton showers for hadron collisions so as to achieve next-to-leading logarithmic (NLL) accuracy across a broad set of observables. We do so specifically for colour singlet production. Relative to the existing…
We present first results of resumming soft gluon effects in a simulation of high energy collisions beyond the leading-colour approximation. We work to all orders in QCD perturbation theory using a new parton branching algorithm. This…
In recent years, work has gone into developing deep interpretable methods for image classification that clearly attributes a model's output to specific features of the data. One such of these methods is the Prototypical Part Network…
We study contributions from nonperturbative effects and parton showering in NLO event generators, and present applications to jet final states. We find pT-dependent and rapidity-dependent corrections which can affect the shape of observed…
We present a Monte-Carlo program for the simulation of polarized deep inelastic scattering at next-to-leading order in QCD matched to parton shower programs building on an existing implementation of the unpolarized case in the POWHEG BOX…
We present new sets of nuclear parton distribution functions (nPDFs) at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO). Our analyses are based on deeply inelastic scattering data with charged-lepton and neutrino beams…
We apply the Lagrange Multiplier method to study the uncertainties of physical predictions due to the uncertainties of parton distribution functions (PDFs), using the cross section for W production at a hadron collider as an archetypal…
This paper is about reducing the cost of building good large-scale 3D reconstructions post-hoc. We render 2D views of an existing reconstruction and train a convolutional neural network (CNN) that refines inverse-depth to match a…
Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown…
Applications of neural networks to data analyses in natural sciences are complicated by the fact that many inputs are subject to systematic uncertainties. To control the dependence of the neural network function to variations of the input…