Related papers: Extraction of the Sivers function with deep neural…
The Sivers function is extracted from HERMES data on single spin asymmetries in semi-inclusive deeply inelastic scattering. The result is used for making predictions for the Sivers effect in the Drell-Yan process.
We perform the global analysis of polarized Semi-Inclusive Deep Inelastic Scattering (SIDIS), pion-induced polarized Drell-Yan (DY), and $W^\pm/Z$ boson production data and extract the Sivers function for $u$, $d$, $s$ and for sea-quarks.…
We perform a global fit of the available polarized Semi-Inclusive Deep Inelastic Scattering (SIDIS), polarized pion-induced Drell-Yan (DY) and $W^\pm/Z$ boson production data at N$^3$LO and NNLO accuracy of the Transverse Momentum Dependent…
This paper establishes statistical properties of deep neural network (DNN) estimators under dependent data. Two general results for nonparametric sieve estimators directly applicable to DNN estimators are given. The first establishes rates…
Building on the first-ever application of neural networks in TMD phenomenology: "Extraction of the Sivers function with deep neural networks", we now present a momentum space, physics-informed deep learning framework for the direct…
We calculate the Sivers function in semi-inclusive deep inelastic scattering (SIDIS) and in the Drell-Yan process (DY) by employing the quasi-classical Glauber-Mueller/ McLerran-Venugopalan approximation. Modeling the hadron as a large…
The Sivers distributions recently extracted from semi-inclusive deep inelastic scattering data [1] are used to compute estimates for Sivers asymmetries in Drell-Yan processes which are being planned at several facilities (RHIC, COMPASS,…
Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Formally, DNNs are complicated vector-valued functions which come in a variety of sizes and applications. Unfortunately, modern DNNs have been…
Polarised Semi-Inclusive Deep Inelastic Scattering (SIDIS) processes allow to study Transverse Momentum Dependent partonic distributions (TMDs), which reveal a non trivial three dimensional internal structure of the hadrons in momentum…
New data on the Sivers azimuthal asymmetry measured in semi-inclusive deep-inelastic scattering processes have recently been released by the COMPASS Collaboration at CERN. Their increased precision and their particular binning, in terms of…
This paper explores the use of deep neural networks for semiparametric estimation of economic models of maximizing behavior in production or discrete choice. We argue that certain deep networks are particularly well suited as a…
While deep neural networks (DNNs) have been increasingly applied to choice analysis showing high predictive power, it is unclear to what extent researchers can interpret economic information from DNNs. This paper demonstrates that DNNs can…
Deep neural networks (DNNs) are widely used in pattern-recognition tasks for which a human comprehensible, quantitative description of the data-generating process, e.g., in the form of equations, cannot be achieved. While doing so, DNNs…
We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits the full kinematic information of both the scattered electron…
The most recent data on the weighted transverse single spin asymmetry $A_{UT}^{\sin(\phi_h-\phi_S)}$ from HERMES and COMPASS collaborations are analysed within LO parton model with unintegrated parton distribution and fragmentation…
Deep neural networks (DNNs) demonstrate outstanding performance across most computer vision tasks. Some critical applications, such as autonomous driving or medical imaging, also require investigation into their behavior and the reasons…
A striking prediction of QCD on the properties of the novel Transverse Momentum Dependent (TMD) distribution functions is that the time-reversal odd Sivers and Boer-Mulders functions extracted from semi-inclusive deep-inelastic scattering…
A comparison is given of the various recently published extractions of the Sivers functions from the HERMES and COMPASS data on single-transverse spin asymmetries in semi-inclusive deeply inelastic scattering.
The Sivers function is extracted from HERMES data on single spin asymmetries in semi-inclusive deeply inelastic scattering. Our analysis use a simple Gaussian model for the distribution of transverse parton momenta, together with the flavor…
Inspired by the remarkable learning and prediction performance of deep neural networks (DNNs), we apply one special type of DNN framework, known as model-driven deep unfolding neural network, to reconfigurable intelligent surface…