Related papers: Reconstruction of Gravitational Form Factors using…
The $\rho$ meson gravitational form factors are studied based on a light-front constituent quark model which has been successfully employed to calculate its generalized parton distributions and some low-energy observables. The distributions…
We calculate gravitational form factors of vector mesons using a holographic model of QCD. These provide restrictions on the generalized parton distributions of vector mesons, via the sum rules connecting stress tensor form factors to GPDs.…
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of…
We extend our relativistic theory of gravitational structure of composite hadrons to obtain the pion gravitational form factors at large momentum transfers. The approach was used in the case of intermediate region of the variable in our…
We propose an efficacious approach to derive the generalized parton distributions for the pion and proton, based upon prior knowledge of their respective parton distribution functions (PDFs). Our method leverages on integral representations…
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 review theoretical approaches to form factors that arise in heavy-meson decays and are hadronic expressions of non-perturbative QCD. After motivating their origin in QCD factorisation, we retrace their evolution from quark-model…
We present a concise, self-contained derivation of diffusion-based generative models. Starting from basic properties of Gaussian distributions (densities, quadratic expectations, re-parameterisation, products, and KL divergences), we…
We discuss how to calculate form factors using a holographic model of QCD, mainly focusing on vector, axial, and pseudoscalar mesons. We illustrate the techniques on gravitational form factors (which are useful for constraining the…
Proton matrix elements of the QCD energy-momentum tensor (EMT) are expressed by the gravitational form factors. The forward values of the gravitational form factors allow for a decomposition of the proton mass into contributions from quarks…
Acquiring high-quality data for training discriminative models is a crucial yet challenging aspect of building effective predictive systems. In this paper, we present Diffusion Inversion, a simple yet effective method that leverages the…
Proton and neutron electric and magnetic form factors are the primary characteristics of their spatial structure and have been studied extensively over the past half-century. At large values of the momentum transfer $Q^2$ they should reveal…
We present a variational autoencoder framework for learning and generating configurations of structured polymer globules from distance matrices. We used coarse-grained molecular dynamics to sample polyethylene structures, which we used as…
Learning to sample from complex unnormalized distributions is a fundamental challenge in computational physics and machine learning. While score-based and variational methods have achieved success in continuous domains, extending them to…
We propose a reconstruction of the full $(x, \xi, t)$ dependence of unpolarized isovector proton generalized parton distributions (GPDs) $H^{u-d}$ and $E^{u-d}$ from lattice QCD data in the pseudo-distribution formalism. For the first time,…
We consider the approximation of functions by 2-layer neural networks with a small number of hidden weights based on the squared loss and small datasets. Due to the highly non-convex energy landscape, gradient-based training often suffers…
Direct prediction of material properties from microstructures through statistical models has shown to be a potential approach to accelerating computational material design with large design spaces. However, statistical modeling of highly…
The modern aerodynamic optimization has a strong demand for parametric methods with high levels of intuitiveness, flexibility, and representative accuracy, which cannot be fully achieved through traditional airfoil parametric techniques. In…
Generative models have emerged as powerful priors for solving inverse problems. These models typically represent a class of natural signals using a single fixed complexity or dimensionality. This can be limiting: depending on the problem, a…
Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings. We propose a novel general-purpose framework…