Related papers: Self-Organizing Maps Parametrization of Deep Inela…
We apply the soft-collinear effective theory (SCET) to deep inelastic scattering near the endpoint region. The forward scattering amplitude, and the structure functions are shown to factorize as a convolution of the Wilson coefficients, the…
Neutrino Deep Inelastic Scattering on nuclei is an essential process to constrain the strange quark parton distribution functions in the proton. The critical component on the way to using the neutrino DIS data in a proton PDF analysis is…
A review is presented on all higher order QCD corrections to deep inelastic structure functions. The implications of these corrections for polarized and unpolarized deep inelastic lepton-hadron scattering will be discussed.
We will give a review of the computation of exact next-to-leading order corrections to heavy quark production in deep inelastic lepton-hadron scattering and discuss the progress made in this field over the past ten years. In this approach,…
Originating from image recognition, methods of machine learning allow for effective feature extraction and dimensionality reduction in multidimensional datasets, thereby providing an extraordinary tool to deal with classical and quantum…
The size of non-perturbative corrections to high E_T jet production in deep-inelastic scattering is reviewed. Based on predictions from fragmentation models, hadronization corrections for different jet definitions are compared and the model…
We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM). DNM jointly learns an embedding of the input data and a mapping from the…
We analyse LEP and PETRA data on single inclusive charged hadron cross-sections to establish new sets of Next-to-Leading order Fragmentation Functions. Data on hadro-production of large-$p_{\bot}$ hadrons are also used to constrain the…
Self-organizing maps (SOMs) are a technique that has been used with high-dimensional data vectors to develop an archetypal set of states (nodes) that span, in some sense, the high-dimensional space. Noteworthy applications include weather…
Distributional data have become increasingly prominent in modern signal processing, highlighting the necessity of computing optimal transport (OT) maps across multiple probability distributions. Nevertheless, recent studies on neural OT…
We construct a model for inelastic neutrino- and electron-nucleon scattering cross sections using effective leading order parton distribution functions with a new scaling variable $\xi_w$. Non-perturbative effects are well described using…
Over the past two decades, intense experimental efforts have focused on measuring observables that contribute to a three-dimensional description of the nucleon. Generalized Parton Distributions provide complementary insights into the…
We consider the inverse problem of determining the geometry of penetrable objects from scattering data generated by one incident wave at a fixed frequency. We first study an orthogonality sampling type method which is fast, simple to…
We present {\tt GKG18-DPDFs}, a next-to-leading order (NLO) QCD analysis of diffractive parton distribution functions (diffractive PDFs) and their uncertainties. This is the first global set of diffractive PDFs determined within the {\tt…
Recently, the ZEUS collaboration has reported on several remarkable properties of events with a large rapidity gap in deep inelastic scattering. We suggest that the mechanism underlying these events is the scattering of electrons off lumps…
We present a new leading and next-to-leading QCD analysis of the world data on inclusive polarized deep inelastic scattering. A new set of polarized parton densities is extracted from the data and the sensitivity of the results to the newly…
Here we present a new non-parametric approach to density estimation and classification derived from theory in Radon transforms and image reconstruction. We start by constructing a "forward problem" in which the unknown density is mapped to…
We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning…
Polarized parton distribution functions are determined by using asymmetry A_1 data from longitudinally polarized deep inelastic scattering experiments. From our \chi^2 analysis, polarized u-valence, d-valence, antiquark, and gluon…
We present a method for extraction of detailed information on polarized quark densities from semi-inclusive deep inelastic scattering l+N -> l+h+X, in both LO and NLO QCD without any assumptions about fragmentation functions and polarized…