相关论文: Deep Neural Network extraction of Unpolarized Tran…

We present an extraction of unpolarized quark transverse-momentum-dependent parton distribution functions (TMD PDFs) from Drell-Yan data within a Bayesian inference framework, incorporating artificial intelligence at multiple stages of the…

Transverse momentum dependent distribution and fragmentation functions of unpolarized quarks inside unpolarized protons are extracted, for the first time, through a simultaneous analysis of semi-inclusive deep-inelastic scattering,…

The QCD evolution of the unpolarized Transverse Momentum Dependent (TMD) distribution functions and of the Sivers functions have been discussed in recent papers. Following such results we reconsider previous extractions of the Sivers…

We present the extraction of unpolarized quark transverse momentum dependent parton distribution functions (TMDPDFs) and the non-perturbative part of TMD evolution kernel from the global analysis of Drell-Yan and $Z$-boson production data.…

We present the first extraction of transverse-momentum-dependent distributions of unpolarised quarks from experimental Drell-Yan data using neural networks to parametrise their nonperturbative part. We show that neural networks outperform…

We present an extraction of unpolarized partonic transverse momentum distributions (TMDs) from a simultaneous fit of available data measured in semi-inclusive deep-inelastic scattering, Drell-Yan and Z-boson production. To connect data at…

We perform the first simultaneous global QCD extraction of the transverse momentum dependent (TMD) parton distribution functions and the TMD fragmentation functions in nuclei. We have considered the world set of data from semi-inclusive…

We present the extraction of unpolarized transverse momentum dependent parton distributions functions (TMDPDFs) and Collins-Soper kernel from the fit of Drell-Yan and weak-vector boson production data. The TMDPDF are parameterized, as…

Deep Neural Networks (DNNs) are a powerful and flexible tool for information extraction and modeling. In this study, we use DNNs to extract the Sivers functions by globally fitting Semi- Inclusive Deep Inelastic Scattering (SIDIS) and…

We present the first simultaneous global QCD analysis of unpolarized transverse momentum dependent (TMD) and collinear parton distribution functions (PDFs) in the proton. Our study incorporates data from deep-inelastic scattering,…

We map the distribution of unpolarized quarks inside a unpolarized pion as a function of the quark's transverse momentum, encoded in unpolarized Transverse Momentum Distributions (TMDs). We extract the pion TMDs from available data of…

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…

We discuss the evolution of the eight leading twist transverse momentum dependent parton distribution functions, which turns out to be universal and spin independent. By using the highest order perturbatively calculable ingredients at our…

We discuss in detail a method to study transverse momentum dependent parton distribution functions (TMDs) using lattice QCD. To develop the formalism and to obtain first numerical results, we directly implement a bi-local quark-quark…

We present a first lattice QCD calculation of the unpolarized nucleon's isovector transverse-momentum-dependent parton distribution functions (TMDPDFs), which are essential to predict observables of multi-scale, semi-inclusive processes in…

We present a fit of the transverse momentum spectrum for Drell-Yan and semi-inclusive deep inelastic scattering data, based on transverse momentum dependent (TMD) factorization at N$^4$LL accuracy. Our analysis shows good agreement with the…

Transverse momentum dependent parton distribution functions (TMDPDFs) encode information about the intrinsic motion of quarks inside the nucleon. They are important non-perturbative ingredients in our understanding of, e.g., azimuthal…

We provide a concise overview on transverse momentum dependent (TMD) parton distribution functions, their application to topical issues in high-energy physics phenomenology, and their theoretical connections with QCD resummation, evolution…

We give the first provably efficient algorithms for learning neural networks with distribution shift. We work in the Testable Learning with Distribution Shift framework (TDS learning) of Klivans et al. (2024), where the learner receives…

Extracting information on the three-dimensional structure of hadrons from transverse-momentum data is a challenging task, requiring the separation of perturbative contributions under QCD control from genuinely non-perturbative effects,…